Volitional closed-loop neurostimulation following brain computer interface neurofeedback training

Abstract

Devices, systems, software, and methods are provided for volitional neurostimulation using neurofeedback for treatment of neurological and psychiatric disorders. Methods are provided for training patients to self-modulate a neural signal that can be used to volitionally control their neurostimulation. The methods use a brain computer interface connected to a display, wherein magnitude of a neural signal is represented as the height of a graphical object on the display. The subject increases or decreases the amplitude of the stimulation delivered by adjusting the vertical positioning of the graphical object on the display to reach a selected target. This volitional neurostimulation technique should be applicable across a range of different neurological and psychiatric conditions to support personalized control of neurostimulation.

Description

VOLITIONAL CLOSED-LOOP NEUROSTIMULATION FOLLOWING BRAIN COMPUTER INTERFACE NEUROFEEDBACK TRAININGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit under 35 U.S.C. § 119(e) of provisional application 63 / 761,509, filed February 21, 2025, and provisional application 63 / 880,955, filed September 12, 2025, which applications are hereby incorporated by reference in their entireties.BACKGROUND OF THE INVENTION

[0002] Neurostimulation is an effective therapy for multiple advanced neurological and psychiatric disorders. However, conventional neurostimulation has constant stimulation parameters which cannot adjust to the dynamic clinical pattern that patients experience. Adaptive neurostimulation with changing stimulation parameters is emerging for personalized and context-dependent neuromodulation therapy; however, detection of reliable biomarkers that capture the multidimensional nature of some complex symptoms can be challenging. Thus, there remains a need to develop alternative approaches to improve closed loop neurostimulation therapies for treatment of neurological and psychiatric disorders.SUMMARY OF THE INVENTION

[0003] Devices, systems, software, and methods are provided for volitional neurostimulation using neurofeedback for treatment of neurological and psychiatric disorders. Methods are provided for training patients to self-modulate a neural signal that can be used to volitionally control their neurostimulation. The methods use a brain computer interface connected to a display, wherein magnitude of a neural signal is represented as the height of a graphical object on the display. The subject increases or decreases the amplitude of the stimulation delivered by adjusting the vertical positioning of the graphical object on the display to reach a selected target.

[0004] In an exemplary embodiment, patients with Parkinson’s disease are trained to modulate their intracranial cortical beta signal using at home neurofeedback. The cortical beta signal, represented as the height of a plane in a brain computer interface, is used as an input biomarker for closed-loop neurostimulation. By modulating the brain signal to reach a certain threshold, patients voluntarily increase or decrease the amplitude of their neurostimulation in the absence of motor movement. This volitional neurostimulation technique should be applicable across a range of different neurological and psychiatric conditions to support personalized control of neurostimulation.

[0005] In one aspect, a method for treating a neurological disorder or psychiatric disorder in a subject is provided, the method comprising: positioning a neural recording electrode at a first location in or near a brain region of the subject to record neural signal data; positioning a stimulator to deliver stimulation to a second location in the subject; connecting a brain computer interface (BCI) to the neural recording electrode and a data receiving device, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to the data receiving device, wherein the data receiving device comprises a processor coupled to a display; recording the neural signal data using the neural recording electrode while displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data; and delivering stimulation to the second location in the subject using the stimulator, wherein the subject voluntarily increases or decreases the amplitude of the stimulation delivered to the second location in the subject by adjusting the vertical positioning of the graphical object on the display.

[0006] In certain embodiments, the graphical object resembles an airplane.

[0007] In certain embodiments, the display further displays a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background. In some embodiments, the horizontal speed of the graphical object across the background is constant.

[0008] In certain embodiments, the method further comprises training the subject to adjust amplitude of the stimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object on the display, wherein the subject adjusts the vertical positioning of the graphical object to reach a selected threshold.

[0009] In certain embodiments, the display further displays a target having a target shape, wherein said training comprises having the subject adjust amplitude of the stimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object such that the graphical object hits the target on the display. In some embodiments, the target is positioned at a position in a range from 25% to 75% of the height of the display. In some embodiments, the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.

[0010] In certain embodiments, the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.

[0011] In certain embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

[0012] In certain embodiments, the method further comprises: disconnecting the BCI from the neural recording electrode and the data receiving device after the training; and delivering stimulation to thesecond location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.

[0013] In certain embodiments, the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array. In some embodiments, the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.

[0014] In certain embodiments, the method further comprises positioning a stimulation electrode at the second location, wherein the stimulation electrode is connected to the stimulator. In some embodiments, the stimulation electrode is a non-brain penetrating surface electrode array or a brainpenetrating electrode array. In some embodiments, the stimulation electrode is positioned in a brain region, in a subgaleal space, on a head, in an epidural space, near a peripheral nerve, or near a muscle of the subject.

[0015] In certain embodiments, the stimulation comprises electrical stimulation, magnetic stimulation, or ultrasound stimulation.

[0016] In certain embodiments, the stimulation comprises deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation. In some embodiments, the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

[0017] In certain embodiments, delivering electrical stimulation comprises delivering electrical stimulation to a brain region, spinal cord, peripheral nerve, or muscle of the subject. In some embodiments, the peripheral nerve is a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

[0018] In certain embodiments, the stimulator is an ultrasound transducer (e.g., for delivering pulsed focused ultrasound simulation).

[0019] In certain embodiments, method further comprises positioning a magnetic coil at the second location, wherein the magnetic coil is connected to the stimulator (e.g., for delivering transcranial magnetic stimulation).

[0020] In certain embodiments, the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.

[0021] In certain embodiments, the movement disorder is Parkinson’s disease or dystonia. In some embodiments, the first location is a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and the second location is a subthalamic nucleus brain region or a globus pallidus internus brain region. In some embodiments, the neural signal is a cortical beta signal. In some embodiments, the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz. In some embodiments, the vertical position of the graphical object on the display is controlled by magnitude of cortical beta power. In some embodiments, the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject. In certain embodiments, the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power. In some embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power. In some embodiments, the training comprises down-regulating or up-regulating the cortical beta power by adjusting the vertical position of the graphical object on the display. In some embodiments, the training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target. In certain embodiments, the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold. In certain embodiments, the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold.

[0022] In certain embodiments, the subject is required to remain immobile during the training. In certain embodiments, the method further comprises: recording a video of the subject during the training; and checking the video to determine if the subject remained immobile during the training.

[0023] In certain embodiments, the method further comprising setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.

[0024] In certain embodiments, the psychiatric disorder is a mood disorder.

[0025] In certain embodiments, the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.

[0026] In certain embodiments, the stimulation is applied unilaterally or bilaterally.

[0027] In certain embodiments, the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations. In someembodiments, the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neural oscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.

[0028] In certain embodiments, the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model.

[0029] In certain embodiments, the method further comprises automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject. In some embodiments, the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.

[0030] In certain embodiments, the method further comprises: displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; and subsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time. In some embodiments, the method further comprises delivering stimulation using settings adjusted by the subject without displaying the graphical object on the display based on the training to achieve the learned neural state.

[0031] In certain embodiments, the data receiving device Is a computer or a handheld device. In some embodiments, the handheld device is a cell phone or tablet.

[0032] In certain embodiments, the data receiving device is a cloud computing system.

[0033] In certain embodiments, the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.

[0034] In certain embodiments, the stimulation promotes survival of motor neurons, strengthens weakened cortico-spinal connections, improves function or re-establishes function of a neural pathway, or any combination thereof.

[0035] In certain embodiments, the neural signal data comprises motor neuron data, and wherein said delivering the stimulation comprises delivering stimulation to the central nervous system, peripheral nervous system, or combination thereof.

[0036] In certain embodiments, delivering the stimulation comprises vagus nerve stimulation, functional electrical stimulation, deep brain stimulation, or any combination thereof.

[0037] In certain embodiments, the subject is recovering from a stroke.

[0038] In certain embodiments, the method further comprises detecting cortico-spinal connectivity changes, motor evoked potential changes, connectivity or neuroplasticity changes, or any combination thereof resulting from said delivering the stimulation.

[0039] In certain embodiments, the method is performed while the subject is at home with remote monitoring of the subject by a clinician, optionally wherein the clinician interacts with the subject through telemedicine or telehealth. In some embodiments, the method further comprises cloudbased data transmission of the neural signal data to a data receiving device used by the clinician. In some embodiments, the method further comprises remote parameter adjustment by the clinician.

[0040] In certain embodiments, method further comprises cloud-based data transmission of a computer program for performing the computer-implemented method to the data receiving device used by the subject.

[0041] In another aspect, a computer-implemented method is provided, the computer performing steps comprising: receiving recorded neural signal data from a neural recording electrode positioned at a first location in or near a brain region of the subject, wherein the neural recording electrode is connected to a BCI, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to a data receiving device comprising a processor coupled to a display; displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data; adjusting settings of a stimulator based on the subject voluntarily increasing or decreasing the amplitude of the stimulation delivered to the subject by adjusting the vertical positioning of the graphical object on the display; and instructing the stimulator to deliver the stimulation to the subject using the adjusted settings, wherein the stimulation is delivered to a second location in the subject. In certain embodiments, the graphical object resembles an airplane. In some embodiments, the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal. In some embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

[0042] In certain embodiments, the computer-implemented method further comprises displaying a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background. In some embodiments, the horizontal speed of the graphical object across the background is constant.

[0043] In certain embodiments, the computer-implemented method further comprises displaying a target having a target shape to provide training to the subject, wherein said training comprises having the subject adjust amplitude of the stimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object such that the graphical object hitsthe target on the display. In some embodiments, the target is positioned at a position in a range from 25% to 75% of the height of the display. In some embodiments, the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape. After providing the training to the subject, in some embodiments, the computer-implemented method further comprises instructing the stimulator to deliver stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.

[0044] In certain embodiments, wherein the subject has Parkinson’s disease, the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject. In some embodiments, the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power. In some embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power. In some embodiments, the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold. In some embodiments, the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold. In some embodiments, providing the training to the subject comprises displaying the graphical object on the display, wherein the subject down-regulates or up-regulates the cortical beta power by adjusting the vertical position of the graphical object on the display. In some embodiments, the training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target.

[0045] In certain embodiments, the computer-implemented method further comprises: instructing a video recording device to record a video of the subject during the training; and analyzing the video to determine if the subject remained immobile during the training.

[0046] In certain embodiments, the computer-implemented method further comprises: setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.

[0047] In certain embodiments, the computer-implemented method further comprises automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject. In some embodiments, the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.

[0048] In certain embodiments, the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model. In some embodiments, the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output. In some embodiments, the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal. In some embodiments, the machine learning model uses multivariate analysis of the neural signal data. In some embodiments, the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection. In some embodiments, the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination. In some embodiments, the machine learning model uses dimension decomposition to isolate the biomarker from a confound. In some embodiments, the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof. In some embodiments, the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker. In some embodiments, the machine learning model is further used to distinguish movement artifacts from the neural signal. In some embodiments, the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data. In some embodiments, the machine learning model uses reinforcement learning threshold adaptation. In some embodiments, the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.

[0049] In certain embodiments, the computer-implemented method further comprises: displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; and subsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time. In some embodiments, the computer- implemented method further comprises instructing the stimulator to deliver the stimulation to the subject using settings adjusted by the subject without displaying the graphical object on the display based on the training to achieve the learned neural state. In some embodiments, the neural state is an induced neuroplasticity state or a patient-generated neural state. In some embodiments, the stimulation induces the neuroplasticity state or the patient-generated neural state. In some embodiments, the computer-implemented method further comprises triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state. Exemplaryassistive devices include, but are not limited to, an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, and a language production device. In some embodiments, the computer-implemented method further comprises delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device. In some embodiments, the computer-implemented method further comprises: delivering transcranial stimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

[0050] In certain embodiments, the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.

[0051] In another aspect, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the computer-implemented method described herein.

[0052] In another aspect, a kit is provided, the kit comprising the non-transitory computer-readable medium and instructions for using the neurofeedback provided through the BCI to volitionally control stimulation for treatment of a neurological disorder or psychiatric disorder.

[0053] In another aspect, a system for treating a neurological disorder or psychiatric disorder in a subject is provided, the system comprising: a neural recording electrode; a stimulator; a data receiving device comprising a processor programmed according to a computer implemented method described herein; a display, wherein the display is connected to the data receiving device; and a brain computer interface (BCI), wherein the BCI is connected to the neural recording electrode and the data receiving device.

[0054] In certain embodiments, the stimulator provides electrical stimulation, magnetic stimulation, or ultrasound stimulation. In some embodiments, electrical stimulation is applied unilaterally or bilaterally. In some embodiments, the stimulator provides deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation (e.g., transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation), spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation. In some embodiments, the stimulator provides peripheral nerve stimulation or peripheral nerve field stimulation to a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciaticnerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

[0055] In certain embodiments, the system further comprises a video recording device.

[0056] In certain embodiments, the system further comprises an external wearable monitor that can acquire accelerometry data, gyroscope data, magnetometer surface electromyographic (sEMG) data, or any combination thereof.

[0057] In certain embodiments, the data receiving device Is a computer or a handheld device (e.g.m cell phone or tablet).

[0058] In certain embodiments, the data receiving device is a cloud computing system.

[0059] In certain embodiments, the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.

[0060] In certain embodiments, the system further comprises a stimulation electrode, wherein the stimulation electrode is connected to the stimulator. In some embodiments, the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0061] In certain embodiments, the system comprises a neural recording electrode adapted for positioning in or near a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and wherein the stimulation electrode is adapted for positioning in a subthalamic nucleus region or a globus pallidus internus region.

[0062] In certain embodiments, the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

[0063] In certain embodiments, the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.

[0064] In certain embodiments, the stimulator of the system is an ultrasound transducer.

[0065] In certain embodiments, the system further comprises a magnetic coil, wherein the magnetic coil is connected to the stimulator.BRIEF DESCRIPTION OF THE DRAWINGS

[0066] FIGS. 1A-1B. Overview of the brain-computer interface (BCI) neurofeedback (NF) study.(FIG. 1A) The BCI airplane simulation game. Two right-handed patients were chronically implanted with bilateral electrocorticography (ECoG) at the sensorimotor cortex and sensing-enabled deep brain stimulation (DBS) lead at the subthalamic nucleus (STN). The vertical position of the plane reflectsthe log beta power at the left primary motor cortex (M 1) channel in real time. The game has ‘rest’ trials and ‘regulate’ trials (see Methods). Participants are required not to make physical movements and are videotaped during the game to check for immobility. (FIG. 1B) Experiment procedure consists of a training phase of 7 NF sessions on constant DBS + a testing phase of 4 volitional DBS (vDBS) sessions. Each NF session consists of 3 blocks, with 10 ‘rest’ trials and 10 ‘regulate’ trials in an interleaved order in each block. Data during the last two NF sessions (i.e. NF 6 and 7) were used to determine the best beta threshold (T) for later aDBS tests. Each aDBS session consists of 2 blocks of ‘constant’ policy, 2 blocks of ‘increase’ vDBS policy, and 2 blocks of ‘decrease’ vDBS policy (see Methods). The order of the 3 policies was counterbalanced across vDBS sessions.

[0067] FIGS. 2A-2B. Performance during the BCI neurofeedback game. (FIG. 2A) Participants’ performance improved across training sessions. (FIG. 2B) Performance across DBS policies during the testing sessions. The score in the game is the number of targets hit by the airplane per block, with a maximum score possible being 150 per block.

[0068] FIGS. 3A-3D. Self-regulation of beta power during the testing sessions. (FIG. 3A) Trial-level cortical power spectral density (PSD) across conditions under constant DBS policy. (FIG. 3B) Triallevel subcortical power spectral density (PSD) across conditions under constant DBS policy. The grey area represents the beta band (13-30 Hz). Error bars and ribbons represent bootstraped 95% confidence intervals at the trial-level averaged values. (FIG. 3C) Distribution separation of cortical beta power between the ‘regulation’ condition and the ‘rest’ condition in real-time temporal resolution (every 0.4s). The dashed vertical line indicates the optimal threshold of beta power to maximize F1 score for each patient. (FIG. 3D) The percentage of time spent in low or high beta state across conditions. Low beta state is when beta power is below the threshold and high beta state is when beta power is above the threshold. Statistical significance: *p < .05, **p < 1e-3, ***p < 1e-10.

[0069] FIGS. 4A-4D. Volitional adaptive DBS with BCI neurofeedback. (FIG. 4A) Trial-level average cortical beta power in the ‘regulation’ and ‘rest’ conditions. Beta power in the ‘regulation’ condition was significantly lower than in the ‘rest’ condition. (FIG. 4B) Time taken for cortical beta to reach the personalized threshold in the ‘regulation’ and ‘rest’ trials. The time used in the ‘regulation’ trials was significantly lower than in the ‘rest’ trials. (FIG. 4C) Temporal dynamics of cortical beta power, its adaptive state, and stimulation amplitude under the constant, increase, and decrease policy. State 0 indicates a low beta state and state 1 indicates a high beta state. (FIG. 4D) Average stimulation amplitude participants received across trial conditions under the constant, increase, and decrease policy. Statistical significance: **p < 1e-3, ***p < 1e-10.

[0070] FIGS. 5A-5B. Motor performance in a key tapping task before and after BCI neurofeedback (NF) blocks during test sessions. (FIG. 5A) Inter-tap interval (s) during the key tapping task acrossDBS policies. (FIG. 5B): Number of errors made during the key tapping task across DBS policies. Patient 1 had only one pre-NF test under constant DBS policy at the very beginning of a session, whereas Patient 2 had a pre-NF test before the NF blocks of each DBS policy. Statistical significance: *p < .05, **p < .01, n.s. = not significant.

[0071] FIG. 6. MDS-UPDRS Section III scores. This motor assessment was conducted by a neurologist through video telemetry at the beginning of the first NF training session (preNF) and at the end of the last NF training session (post NF). A few items were not performed because they require an in-person setting. The recorded videos were scored by another neurologist blinded to experiment conditions.DETAILED DESCRIPTION OF THE INVENTION

[0072] Devices, systems, software, and methods are provided for volitional neurostimulation using neurofeedback for treatment of neurological and psychiatric disorders.

[0073] Before the present devices, systems, software, and methods are described, it is to be understood that this invention is not limited to the particular devices, systems, software, and methods described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0074] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0075] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications arecited. It is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.

[0076] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0077] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "an electrode" includes a plurality of such electrodes and reference to "the electrical signal" includes reference to one or more electrical signals, and so forth.

[0078] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.Definitions

[0079] The term "about," particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0080] The term "neurological disorder" refers to any disorder that impairs function of the nervous system. Neurological disorders include, but are not limited to, movement disorders, sleep disorders, stroke, epilepsy, traumatic brain injuries, dementia, delirium, coma or minimally conscious states, or pain-associated disorders.

[0081] The term "movement disorder" refers to any type of neurological disorder that causes either increased movements or reduced or slow movements. Movement disorders include, but are not limited to, Parkinson's disease, parkinsonism, progressive supranuclear palsy, ataxia, cervical dystonia, chorea, dystonia, essential tremor, functional movement disorder, Huntington's disease, multiple system atrophy, myoclonus, tardive dyskinesia, Tourette syndrome, tremor, restless legs syndrome, and Wilson's disease. Symptoms may include, but art not limited to, tremor, involuntary movements, slowness of movement (bradykinesia), rigidity, postural instability, twisting movements, poor balance, irregularity of movements, stumbling, and difficulty with walking. In some cases, a movement disorder is caused by genetic and / or environmental factors, head trauma, infections,inflammation, metabolic disturbances, toxins, adverse reactions to medications, or stressful life events.

[0082] The term “psychiatric disorder” is used herein to refer to a condition that affects mood and / or behavior of a person suffering from the disorder. Psychiatric disorders include major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), addiction, phobia, anorexia, obsessive-compulsive disorder (OCD), bipolar disorder (BD), disorders of diminished motivation (DDM), and chronic pain.

[0083] The term “affective disorders” is used herein to refer to a group of psychiatric disorders that typically affect mood of the person suffering from such a disorder. Psychiatric disorders that affect the mood are also referred to as mood disorders and are commonly associated with depression and / or anxiety. The main types of affective disorders are depression, bipolar disorder, and anxiety disorder. Symptoms vary by individual, but they typically affect mood. They can range from mild to severe.

[0084] As used herein the term “depression” refers to a mental state of morbid sadness, dejection, or melancholy.

[0085] As used herein the term “anxiety” refers to an uncomfortable and unjustified sense of apprehension that may be diffuse and unfocused and is often accompanied by physiological symptoms.

[0086] As used herein the term “bipolar disorder” refers to a type of affective disorder in which the person suffering from this disorder goes through periods of depression and periods of mania (feeling extremely positive and active).

[0087] As used herein the term “anxiety disorder” refers to a psychiatric disorder characterized by feelings of nervousness, anxiety, and even fear. Anxiety disorders include social anxiety (anxiety caused by social situations), post-traumatic stress disorder (anxiety, fear, and flashbacks caused by a traumatic event), generalized anxiety disorder (anxiousness and fear in general, with no particular cause), panic disorder (anxiety that causes panic attacks), and obsessive-compulsive disorder (obsessive thoughts that cause anxiety and compulsive actions).

[0088] As used herein the term “disorders of diminished motivation” refers to psychiatric disorders characterized by a reduction in motivation, will, and emotional expression. Such disorders often involve a lack of goal-directed behavior and a reduction in speech, movement, and emotional responses. Disorders of diminished motivation include apathy, abulia, and akinetic mutism.

[0089] The term “pain-associated disorder” is used herein to refer to conditions that cause pain in a person suffering from the disorder. The pain may be chronic, idiopathic, nociceptive, inflammatory, visceral or neuropathic pain. Pain-associated disorders include, without limitation, back pain, failedback surgery syndrome, spinal cord injury, spinal stenosis, post-surgical pain, complex regional pain syndrome, arachnoiditis, angina, nerve-related pain (e.g., such as caused by diabetic neuropathy, cancer-related neuropathy, or nerve damage caused by radiation, surgery, or chemotherapy), peripheral vascular disease, pain after an amputation, visceral abdominal pain, perineal pain, multiple sclerosis, arthritis, or chronic leg pain (e.g., sciatica), neck pain, foot pain, or arm pain.

[0090] The terms “individual”, “subject”, “recipient”, and “patient” are used interchangeably herein and refer to any mammalian subject for whom treatment or therapy is desired, particularly humans. "Mammal" for purposes of treatment refers to any animal classified as a mammal, including human and non-human mammals such as non-human primates, including chimpanzees and other apes and monkey species; laboratory animals such as mice, rats, rabbits, hamsters, guinea pigs, and chinchillas; domestic animals such as dogs and cats; and farm animals such as sheep, goats, pigs, horses and cows.

[0091] The term “user” as used herein refers to a person that interacts with a device and / or system disclosed herein for performing one or more steps of the presently disclosed methods. The user may be the patient receiving treatment for a neurological or psychiatric disorder. The user may be a health care practitioner, such as the patient’s physician.

[0092] The terms "treatment", "treating", "treat" and the like are used herein to generally refer to obtaining a desired pharmacologic and / or physiologic effect. The effect can be prophylactic in terms of completely or partially preventing a disease or symptom(s) thereof and / or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and / or adverse effect attributable to the disease. The term “treatment" encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and / or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and / or symptom(s), i.e., arresting their development; or (c) relieving the disease symptom(s), i.e., causing regression of the disease and / or symptom(s). Those in need of treatment include those already inflicted (e.g., those with a neurological or psychiatric disorder) as well as those in which prevention is desired those with a genetic predisposition to developing a neurological or psychiatric disorder, those with increased susceptibility to developing a neurological or psychiatric disorder, those suspected of having a neurological or psychiatric disorder, etc.).

[0093] A therapeutic treatment is one in which the subject is inflicted prior to administration and a prophylactic treatment is one in which the subject is not inflicted prior to administration. In some embodiments, the subject has an increased likelihood of becoming inflicted or is suspected of being inflicted prior to treatment. In some embodiments, the subject is suspected of having an increased likelihood of becoming inflicted.

[0094] The term “responsive” as used herein means that the treatment is having the desired effect such as reducing symptom severity caused by a neurological or psychiatric disorder. When the individual does not improve in response to the treatment, it may be desirable to seek a different therapy or treatment regime for the individual.

[0095] The terms "connected" or "coupled" are used in an operational sense and are not necessarily limited to a direct connection or coupling. For example, two devices or components may be coupled directly, or via one or more intermediary media or devices. As another example, devices may be coupled in such a way that information or data can be passed between them, while not sharing any physical connection with one another. In some cases, two devices or components may be connected by a wire or wirelessly to each other.Methods

[0096] Neurostimulation is an effective therapy for multiple advanced neurological and psychiatric disorders. However, conventional neurostimulation is performed with constant stimulation parameters, which do not adjust to the dynamic clinical pattern that patients experience. Adaptive neurostimulation with changing stimulation parameters is needed for personalized and context- dependent neuromodulation therapy.

[0097] The present disclosure provides methods for volitional neurostimulation using neurofeedback for treatment of neurological and psychiatric disorders. Patients are trained to self-modulate a neural signal that can be used to volitionally control their neurostimulation. To provide neurofeedback, the magnitude of the neural signal is represented as the height of a graphical object on a display. By modulating the magnitude of the neural signal to reach a certain threshold, patients voluntarily increase or decrease the amplitude of their neurostimulation. This volitional neurostimulation technique should be applicable across a range of different neurological and psychiatric disorders to support personalized control of neurostimulation. Various steps and aspects of the methods will now be described in greater detail below.

[0098] The method includes positioning an electrode at a first location in or near a brain region of the subject to record neural signal data (i.e. , neural recording electrode) and positioning a stimulator to deliver stimulation to a second location in the subject. A BCI is connected to the neural recording electrode and a data receiving device, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to the data receiving device, wherein the data receiving device comprises a processor coupled to a display. The neural signal data is recorded using the neural recording electrode while displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by themagnitude of the neural signals to provide neurofeedback to the subject. Stimulation is delivered to the second location in the subject using the stimulator, wherein the subject voluntarily increases or decreases the amplitude of the stimulation delivered to the second location by adjusting the vertical positioning of the graphical object on the display.

[0099] The stimulation may include, without limitation, deep brain stimulation, subgaleal stimulation, transcranial stimulation (e.g., transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation), spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, or neuromuscular electrical stimulation. In some embodiments, an electrode is used to deliver electrical stimulation (i.e., stimulation electrode) to the second location. The position of the stimulation electrode will depend on the target of the electrical stimulation. For example, a stimulation electrode may be placed in a region of the brain for deep brain stimulation, in a subgaleal space for brain stimulation, on the head for transcranial stimulation, in the epidural space for spinal cord stimulation, near a peripheral nerve for peripheral nerve stimulation or peripheral nerve field stimulation, or near a muscle for neuromuscular electrical stimulation.

[0100] For brain stimulation, one or more neural recording electrodes or stimulation electrodes may be positioned at a first brain region or a second brain region, respectively. In some embodiments, the first brain region and the second brain region are the same. In other embodiments, the first brain region and the second brain region are different. The stimulation electrodes and the neural recording electrodes may be non-brain penetrating surface electrodes, extracranial electrodes, for example, subgaleal or skull mounted (in burrhole cap or in case of cranially mounted neurostimulator), subdural electrodes, or brain-penetrating depth electrodes. The electrical stimulation may be applied to the brain using the stimulation electrode with neurostimulation settings tailored to the individual needs of a patient, wherein the patient can voluntarily increase or decrease amplitude of the electrical stimulation by using neurofeedback provided through the brain computer interface, as described herein.

[0101] The site chosen for detection of neural signals may differ for different subjects and may depend on the particular neurological or psychiatric disorder the subject has. In some embodiments, the brain of an individual subject is mapped to identify the optimal location(s) for positioning an electrode for detecting neural signals associated with a symptom of a neurological or psychiatric disorder, as discussed further below

[0102] In an exemplary embodiment for a patient undergoing treatment for Parkinson’s disease, the stimulation electrode may be positioned in a subthalamic nucleus region, a globus pallidus internus region, or other regions of the brain suitable for electrical stimulation. The neural recording electrodemay be positioned at a location in a sensorimotor cortex region, a subthalamic nucleus region, a globus pallidus internus region, a subgaleal region, or other regions of the brain suitable for detecting neural signals associated with intended movement or a symptom of Parkinson’s disease. One or more stimulation electrodes may be positioned in the subthalamic nucleus region and / or globus pallidus internus region, and one or more neural recording electrodes may be positioned in the sensorimotor cortex region, subthalamic nucleus region globus pallidus internus region, and / or subgaleal region. In some embodiments, one or more neural recording electrodes are positioned in a precentral gyrus region, a postcentral gyrus region, or both the precentral gyrus region and the postcentral gyrus region of the contralateral cortex. In some embodiments, one or more stimulation electrodes and one or more neural recording electrodes are positioned in a subthalamic nucleus region. In some embodiments, one or more stimulation electrodes and one or more neural recording electrodes are positioned in a globus pallidus internus region.

[0103] In an exemplary embodiment for a patient undergoing treatment for a psychiatric disorder, the stimulation electrode may be positioned in a ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region, or other regions of the brain suitable for electrical stimulation. The neural recording electrode may be positioned at a location in a right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, right hippocampus region of the brain, or other regions of the brain suitable for detection of neural signals associated with a symptom of the psychiatric disorder. One or more stimulation electrodes may be positioned in the ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region, and one or more neural recording electrodes may be positioned in the right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, or right hippocampus region.

[0104] As used herein, the phrases “an electrode” or “the electrode” refer to a single electrode or multiple electrodes such as an electrode array. As used herein, the term “contact” as used in the context of an electrode in contact with a region of the brain refers to a physical association between the electrode and the region. In other words, a neural recording electrode that is in contact with a region of the brain is physically touching the region of the brain. A stimulation electrode can conduct electricity, for example, to specific targets in the brain, spinal cord, or peripheral nerves. Electrodes used in the methods disclosed herein may be monopolar (cathode or anode) or bipolar (e.g., having an anode and a cathode).

[0105] Positioning a neural recording electrode for recording neural activity at specified region(s) of the brain may be carried out using standard surgical procedures for placement of intra-cranial electrodes. In certain cases, placing the neural recording electrode may involve positioning theelectrode on the surface of the specified region(s) of the brain. For example, for treatment of a movement disorder such as Parkinson’s disease, electrodes may be placed on the surface of the brain at a sensorimotor cortex region (e.g., a precentral gyrus region or postcentral gyrus region), a subthalamic nucleus region, a globus pallidus internus region, or any combination thereof. The electrode may contact at least a portion of the surface of the brain at the sensorimotor cortex region (e.g., a precentral gyrus region or postcentral gyrus region). In some embodiments, the electrode may contact substantially the entire surface area at the sensorimotor cortex region, subthalamic nucleus region, or globus pallidus internus region. In some embodiments, the electrode may additionally contact area(s) adjacent to the sensorimotor cortex region, subthalamic nucleus region, or globus pallidus internus region. In some embodiments, the neural recording electrodes may contact any area of the subthalamic nucleus region, globus pallidus internus region, and / or sensorimotor cortex that allows detection of brain electrical signals from neural activity associated with intended movement of the subject or a symptom of the movement disorder.

[0106] In some embodiments, the electrodes may be placed extracranial ly, for example in the subgaleal space. In some embodiments, the electrodes may be placed in a subdural space over the contralateral cortex or under the scalp. In some embodiments, the neural recording electrode may be contained within a burr hole cap or on the case of the cranially mounted implantable neural stimulator device. In some embodiments, an electrode array, arranged on a planar support substrate, may be used for detecting brain electrical signals from neural activity associated with a symptom of a neurological disorder or psychiatric disorder from one or more of the brain regions specified herein. The surface area of the electrode array may be determined by the desired area of contact between the electrode array and the brain. An electrode for implanting on a brain surface, such as, a surface electrode or a surface electrode array may be obtained from a commercial supplier. A commercially obtained electrode / electrode array may be modified to achieve a desired contact area. In some cases, the non-brain penetrating electrode (also referred to as a surface electrode) that may be used in the methods disclosed herein may be an electrocorticography (ECoG) electrode, a subgaleal electrode, a subdural electrode, or an electroencephalography (EEG) electrode. In certain embodiments, a plurality of electrodes is positioned in an electrode grid for detection of brain electrical signals from neural activity associated with a symptom of a neurological disorder or a psychiatric disorder. In certain embodiments, a plurality of electrodes is positioned at one or more of the brain regions specified herein for detection of brain electrical signals from neural activity associated with a symptom of a neurological disorder or a psychiatric disorder by stereoelectroencephalography (sEEG).

[0107] In certain cases, placing the neural recording electrode at a target area or site (e.g., a sensorimotor cortex region, precentral gyrus region, postcentral gyrus region, right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, right hippocampus region, or frontal lobe region) may involve positioning a brain penetrating electrode (also referred to as depth electrode) in the specified region(s) of the brain. For example, a neural recording electrode may be placed in a sensorimotor cortex region of the brain for recording neural signal associated with a symptom of a movement disorder or in a right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, or right hippocampus region for recording neural signal associated with a symptom of a psychiatric disorder. In some embodiments, the neural recording electrode may additionally contact area(s) adjacent to a sensorimotor cortex region, right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, or right hippocampus region of the brain. In some embodiments, an electrode array may be used for detecting neural activity from a cortical area, for example, a central sulcus region, a precentral gyrus region, or postcentral gyrus region, or a combination thereof, as specified herein.

[0108] The depth to which a neural recording electrode is inserted into the brain may be determined by the desired level of contact between the electrode array and the brain. A brain-penetrating electrode array may be obtained from a commercial supplier. A commercially obtained electrode array may be modified to achieve a desired depth of insertion into the brain tissue.

[0109] Positioning an electrode for delivering electrical stimulation to the brain may be carried out using standard surgical procedures for placement of electrodes for deep brain stimulation. For example, the stimulation electrode may be placed in a subthalamic nucleus region, globus pallidus internus region, or other intracranial region for treatment of a movement disorder. Alternatively, the stimulation electrode may be placed in a ventral capsule / ventral striatum region, subgenual cingulate region, orbitofrontal cortex region, or other intracranial region for treatment of a psychiatric disorder. Medical imaging using, for example, magnetic resonance imaging (MRI) or computerized tomography (CT) may be used to provide guidance for placement of stimulation electrodes and verify correct placement of the stimulation electrodes in the brain. In addition, a neurostimulator that generates electrical pulses is placed under the skin of the chest, typically below the collarbone or in the abdomen. In some embodiments the neurostimulator is cranially mounted. The surgical procedure may involve placing stimulation electrodes within the brain through small holes in the skull. An electrode lead is tunneled under the skin down the neck and under the skin of the chest to connect to a chest implanted neurostimulator.

[0110] Current is supplied by the neurostimulator to the stimulation electrodes. Parameters such as pulse width, shape, frequency, amplitude, pattern, and temporal distribution can be adjusted in response to changes in neural activity recorded by a neural recording electrode. In some embodiments, a closed loop system is used to automatically deliver electrical stimulation in response to detection of neural signals from neural activity associated with a symptom of a neurological disorder or psychiatric disorder. A control algorithm may be used to automate the delivery of electrical stimulation to the brain, spinal cord, or a peripheral nerve in response to detection of neural activity associated with a symptom of a neurological disorder or a psychiatric disorder using parameter settings as adjusted by a subject using neurofeedback provided through a BCI, as described herein. For example, a subject undergoing treatment may voluntarily increase or decrease the amplitude of the stimulation delivered to the brain, spinal cord, or peripheral nerves using neurofeedback provided through a BCI, wherein the subject views the vertical positioning of a graphical object on a display to determine how to adjust the amplitude of the stimulation, wherein the vertical positioning of the graphical object on the display corresponds to the magnitude of a neural signal associated with a symptom of the neurological disorder or psychiatric disorder. This visualization of visual neurofeedback is used during a training phase to train the subject to modulate the electrical stimulation, however, once the patient is trained, ongoing visual neurofeedback may not be needed and may be removed or replaced by simpler feedback such as audio or vibrotactile stimuli.

[0111] In some embodiments, a multi-phase training protocol may be used to train a subject to adjust the parameters of the stimulation to achieve a learned neural state. For example, the multi-phase training protocol may comprise a first phase (Phase 1, Acquisition), during which the subject is trained using the BCI with constant, high-salience visual feedback; a second phase (Phase 2, Consolidation), during which the BCI provides intermittent feedback; and a third phase (Phase 3, Internalization), during which stimulation is triggered by the learned neural state without the subject using displayed biofeedback from the BCI.

[0112] Electrical stimulation may be applied using a single electrode, electrode pairs, or an electrode array. In some embodiments, the number of electrodes used to deliver electrical stimulation ranges from 8 to 32, including any number of electrodes in this range such as 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, or 32 electrodes. In some embodiments, the electrical stimulation is applied to more than one site in the subthalamic nucleus or globus pallidus internus for treatment of a movement disorder or to more than one site in the ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region for treatment of a psychiatric disorder. The site to which the electrical stimulation is applied may be alternated or otherwise spatially or temporally patterned. Electrical stimulation may be applied to the sites simultaneously or sequentially. The site chosen forstimulation may differ for different subjects and will depend on mapping of the brain of an individual subject to identify the optimal location for positioning an electrode for delivery of electrical stimulation to treat a symptom of a neurological disorder or a psychiatric disorder.

[0113] In some embodiments, an electrode array arranged on a planar support substrate may be used for electrically stimulating the subthalamic nucleus or globus pallidus internus for treatment of a movement disorder or the ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region for treatment of a psychiatric disorder. The surface area of the electrode array may be determined by the desired area of contact between the electrode array and the subthalamic nucleus, globus pallidus internus, ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region. In some cases, cylindrical electrode arrays, paddlestyle electrode arrays, or plate-style electrode arrays may be used in the methods disclosed herein for deep brain stimulation. Such stimulation electrode arrays for implanting in the brain, may be obtained from a commercial supplier. A commercially obtained el ectrode / el ectrode array may be modified to achieve a desired contact area.

[0114] The precise number of stimulation electrodes or neural recording electrodes contained in an electrode array (e.g., for electrical stimulation or detection of neural activity) may vary. In certain aspects, an electrode array may include two or more electrodes, such as 3 or more, including 4 or more, e.g., about 3 to 6 electrodes, about 6 to 12 electrodes, about 12 to 18 electrodes, about 18 to 24 electrodes, about 24 to 30 electrodes, about 30 to 48 electrodes, about 48 to 72 electrodes, about 72 to 96 electrodes, or about 96 or more electrodes. The electrodes may be arranged into a regular repeating pattern (e.g., a grid, such as a grid with about 1 cm spacing between electrodes), or no pattern. An electrode that conforms to the target site for optimal delivery of electrical stimulation may be used. One such example is a single multi contact electrode with eight contacts separated by 2% mm. Each contract would have a span of approximately 2 mm. Another example is an electrode with two 1 cm contacts with a 2 mm intervening gap. Yet further, another example of an electrode that can be used in the present methods is a 2 or 3 branched electrode to cover the target site. Each one of these three-pronged electrodes has four 1-2 mm contacts with a center to center separation of 2 of 2.5 mm and a span of 1.5 mm.

[0115] The size of each electrode may also vary depending upon such factors as the number of electrodes in the array, the location of the electrodes, the material, the age of the patient, and other factors. In certain aspects, an electrode array has a size (e.g., a diameter) of about 5 mm or less, such as about 4 mm or less, including 4 mm-0.25 mm, 3 mm-0.25 mm, 2 mm-0.25 mm, 1 mm-0.25 mm, or about 3 mm, about 2 mm, about 1 mm, about 0.5 mm, or about 0.25 mm.

[0116] In certain embodiments, the method further comprises mapping the brain of the subject to optimize positioning of an electrode for applying electrical stimulation. Positioning of a stimulation electrode is optimized to maximize clinical responses to electrical stimulation for treating a symptom of a neurological disorder or psychiatric disorder. In some embodiments, the subthalamic nucleus region, globus pallidus internus region, or other regions of the brain are mapped to determine optimal positioning of stimulation electrodes for treating a movement disorder (e.g., Parkinson’s disease or dystonia). In some embodiments, the ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region, or other regions of the brain are mapped to determine optimal positioning of stimulation electrodes for treating a psychiatric disorder.

[0117] The placement of stimulation electrodes differs for different neurostimulation methods. For example, for spinal cord stimulation (SCS) to relieve pain, an electrode is positioned in the epidural space for delivering electrical stimulation to the spinal cord. The placement of the SCS electrodes depends on the location where the subject is experiencing pain and may be carried out using standard surgical procedures for placement of SCS electrodes. SCS electrodes should be placed to allow stimulation of nerves in the area where pain is felt. The SCS electrodes are placed at the appropriate level along the spine to relieve pain. For example, the electrode may be placed in the epidural space between vertebrae T9 to T10 to relieve lower back pain, at lower thoracic levels for lower limb pain, and at cervical levels for upper limb pain. Paddle electrodes or percutaneous electrodes may be used for spinal cord stimulation. Fluoroscopy or ultrasound may be used to provide guidance for placement of the SCS electrodes. In addition, a pulse generator is placed under the skin, typically near the buttocks or abdomen. The surgical procedure may involve an incision over the spine for placement of the SCS electrodes. An electrode lead is tunneled under the skin and connected to the pulse generator. A subcutaneous pocket can be formed in the abdominal-flank area or the upper buttock for implantation of the pulse generator.

[0118] For peripheral nerve stimulation (PNS), an electrode is positioned near a peripheral nerve for delivering electrical stimulation to a nerve. The PNS electrode is typically placed 0.5 cm to 1 cm from a target nerve. Ultrasound may be used for identification of the target nerve and to guide placement of the PNS electrode. The placement of PNS electrodes depends on the location where the subject is experiencing pain. PNS electrodes should be placed to allow stimulation of nerves in the area where pain is felt without causing muscle contraction. After placement, the PNS electrode is connected to an external stimulator that delivers electrical current through the PNS electrode to peripheral nerves to disrupt pain signals. In some embodiments, peripheral nerve stimulation is delivered to a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, aphrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

[0119] Peripheral nerve field stimulation (PNFS) is similar to PNS, but can be used to stimulate a wider area around a peripheral nerve than PNS. In contrast to PNS, which typically targets the major, deeper peripheral nerves causing pain, PNFS targets small nerve fibers under the skin in an area of pain and in surrounding tissue. PNFS electrodes are placed superficially in the skin in an area of pain to create an electrical field around the small nerve fibers. After placement, the PNFS electrodes are connected to a stimulator device that delivers electrical pulses that interfere with pain signals. Either PNS or PNFS can be used to treat chronic pain conditions such as, but not limited to, back pain, neck pain, leg pain, neuropathic pain, diabetic neuropathy, complex regional pain syndrome, post-amputation pain, occipital neuralgia and other headache syndromes, and trigeminal neuralgia.

[0120] For neuromuscular electrical stimulation (NMES), an electrode is positioned near a muscle for delivering electrical stimulation to the muscle. NMES electrodes are placed on the skin over a target muscle or muscle group typically with longitudinal placement along muscle fibers. Self- adhesive electrodes may be used to attach electrodes to the surface of the skin over a target muscle. NMES electrodes are preferably not placed over joints or bony prominences to avoid causing pain during electrical stimulation. After placement of a NMES electrode, an electrical stimulator is attached to the NMES electrode. NMES can be used to treat various neurological disorders such as, but not limited to, stroke, spinal cord injury, muscle weakness from disuse or surgery, and musculoskeletal issues such as strains, sprains, and pain from conditions like arthritis. NMES can also be used for rehabilitation after orthopedic procedures, to manage spasticity, prevent muscle atrophy, and improve circulation and motor coordination.

[0121] Transcranial stimulation uses direct current, alternating current, or magnetic fields for neurostimulation of the brain. For transcranial direct current stimulation, electrodes are placed on the scalp to deliver direct current for neurostimulation of the brain. The electrodes are connected to a stimulator device that delivers constant current. For alternating current transcranial stimulation, electrodes are placed on the scalp to deliver alternating electrical current for neurostimulation of the brain. The electrodes are connected to a stimulator device that delivers alternating current. The electrodes are placed over a target region to be stimulated. In some cases, multiple electrodes are placed over multiple target regions. Smaller sized electrodes provide more focused stimulation, whereas larger electrodes spread stimulation over a larger area. A headstrap may be used to help hold electrodes in place. To produce a magnetic field for transcranial magnetic stimulation, a magnetic coil is placed on the scalp to deliver magnetic pulses to targeted brain areas. The magneticcoil is connected to a pulse generator that delivers electric current to the magnetic coil. T ranscranial magnetic stimulation may be performed with magnetic coils having any suitable shape. In some embodiments, transcranial magnetic stimulation is delivered with a figure-of-eight coil, a circular coil, a four-leaf coil, a double-cone coil, a Hesed (H-core) coil, a butterfly coil, a slinky coil, a 3-D differential coil, a deformable coil, a C-shaped coil, a hat-shaped coil, or a coil having a customized shape designed to fit to a subject’s anatomy. A ferromagnetic material may be integrated into the coil to enhance the intensity and focus of the magnetic field. Transcranial stimulation can be used to treat neurological and psychiatric disorders, including, but not limited to, major depressive disorder, obsessive-compulsive disorder (OCD), anxiety disorders such as generalized anxiety disorder (GAD), social anxiety disorder, post-traumatic stress disorder (PTSD), and panic attacks; chronic pain, migraines, stroke, attention-deficit / hyperactivity disorder (ADHD), schizophrenia, movement disorders such as Parkinson's disease and Tourette syndrome; cognitive impairment, and addiction.

[0122] Pulsed focused ultrasound stimulation can be delivered to a target location with an ultrasound transducer that emits bursts of sound waves. The ultrasound transducer may comprise a singleelement transducer with a fixed focal point or a phased-array transducer composed of a series of several single element transducers that can be controlled to move the focal point without moving the device. Pulsed focused ultrasound stimulation can be used for neuromodulation, for example, by targeting a nerve in the central or peripheral nervous system to excite or suppress neural activity. In some embodiments, pulsed focused ultrasound is used to provide neurostimulation for treatment of a neurological or psychiatric disorder such as, but not limited to, Parkinson's disease, essential tremor, epilepsy, Alzheimer's disease, stroke, brain tumors, depression, obsessive-compulsive disorder, schizophrenia, or opioid addiction.

[0123] In certain embodiments, the method further comprises mapping the brain of the subject to optimize positioning of a neural recording electrode. For example, positioning of the neural recording electrode can be optimized to detect brain activity features, including neural signals from neural activity associated with a symptom of a neurological disorder or psychiatric disorder that can be treated with neurostimulation. For example, the levels of overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta) may be correlated with a symptom of a neurological disorder or psychiatric disorder. The neural recording electrodes may be positioned to optimize detection of brain activity in specific frequency ranges that correlate with a symptom of a neurological disorder or psychiatric disorder such as delta frequency neural oscillations in a range from 1 Hz to 4 Hz, theta frequency neural oscillations in a range from 4 Hz to 7 Hz, alpha frequency neural oscillations in a range from 8 Hz to 12 Hz, beta frequency neural oscillations in a range from 13 Hz to 30 Hz, and / or gamma frequency neural oscillations in a range from 60 Hz to 90 Hz. Incertain embodiments, personalized power band features for detecting a symptom of a neurological disorder or psychiatric disorder are identified for a subject.

[0124] Detection of brain activity may be performed by any method known in the art. For example, functional brain imaging of neural activity may be carried out by electrical methods such as electroencephalography (EEG), stereoelectroencephalography (sEEG), electrocorticography (ECoG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), as well as metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). In some embodiments, a sensorimotor cortex region, subthalamic nucleus brain region, globus pallidus internus brain region, right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, right hippocampus region, frontal lobe region, subgaleal region, or other regions are mapped to determine optimal positioning for neural recording electrodes. One or more of these regions may be implanted with neural recording electrodes to measure electrical signals from neural activity, including brain electrical signals from neural activity associated with a symptom of a neurological disorder or psychiatric disorder that can be treated with stimulation (e.g., electrical stimulation, magnetic stimulation, or ultrasound stimulation).

[0125] Closed-loop therapy can be performed with a neurostimulator used in combination with a neural recording device that records brain electrical activity, wherein stimulation (e.g., electrical stimulation, magnetic stimulation, or ultrasound stimulation) is delivered to the subject when a pattern of neural activity associated with a symptom of a neurological disorder or psychiatric disorder is detected. The parameters for applying the stimulation may be initially determined empirically during treatment or may be pre-defined, such as, from a trial study with a subject. Varying stimulation settings may be applied when certain features are detected, including baseline (stimulation off), optimal therapeutic stimulation, modified and ineffective stimulation, and maximum tolerated stimulation to identify personal neural signatures of “a symptom of a neurological disorder or psychiatric disorder” for a patient, which are used to assist with programming of a stimulator to determine optimal therapeutic stimulation parameters for treating the symptom of the neurological disorder or psychiatric disorder.

[0126] The parameters for electrical stimulation may include one or more of frequency, pulse width / duration, duty cycle, intensity / amplitude, pulse pattern, program duration, program frequency, and the like. The electrical stimulation may be applied, for example, to the brain, spinal cord, or peripheral nerves.

[0127] Frequency refers to the pulses produced per second during stimulation and is stated in units of Hertz (Hz, e.g., 60 Hz = 60 pulses per second). The frequencies of electrical stimulation used inthe present methods may vary widely depending on numerous factors and may be determined empirically during treatment of the subject or may be pre-defined. In certain embodiments, the method may involve applying electrical stimulation to the brain at a frequency of 2 Hz - 250 Hz, such as, 25 Hz - 200 Hz, 50 Hz - 250 Hz, 50 Hz -185 Hz, 50 Hz -150 Hz, 75 Hz - 200 Hz, 100 Hz - 200 Hz, 100 Hz - 180 Hz, 100 Hz - 160 Hz, 120 Hz - 150 Hz, or 130 Hz - 140 Hz. In some embodiments, the electrical stimulation to the brain is applied at a frequency of about 120 Hz to about 160 Hz, including any pulse frequency within this range such as 120 Hz, 122 Hz, 124 Hz, 126 Hz, 128 Hz, 130 Hz, 132 Hz, 133 Hz, 134 Hz, 135 Hz, 136 Hz, 137 Hz, 138 Hz, 139 Hz, 140 Hz, 142 Hz, 144 Hz, 146 Hz, 148 Hz, 150 Hz, 152 Hz, 154 Hz, 156 Hz, 158 Hz, or 160 Hz. In some embodiments, noninteger pulse frequencies are used (e.g. 135.2 Hz, 135.4 Hz, etc.).

[0128] The electrical stimulation may be applied in pulses such as a uniphasic or a biphasic pulse.The time span of a single pulse is referred to as the pulse width or pulse duration. The pulse width used in the present methods may vary widely depending on numerous factors (e.g., severity of the disease, status of the patient, and the like) and may be determined empirically or may be pre-defined. In certain embodiments, the method may involve applying an electrical stimulation at a pulse width of about 10 psec - 500 psec, for example, 20 psec - 450 psec, 40 psec - 450 psec, 60 psec - 450 psec, 60 psec - 220 psec, 60 psec - 120 psec, or 60 psec - 90 psec. In some embodiments, the electrical stimulation to the brain is applied at a pulse width of about 60 psec to about 210 psec, including any pulse width within this range such as 60 psec, 65 psec, 70 psec, 75 psec, 80 psec, 85 psec, 90 psec, 95 psec, 100 psec, 105 psec, 110v, 115 psec, 120 psec, 125 psec, 130 psec, 135 psec, 140 psec, 145 psec, 150 psec, 155 psec, 160 psec, 165 psec, 170 psec, 175 psec, 180 psec, 185 psec, 190 psec, 195 psec, 200 psec, 205 psec, 210 psec, 215 psec, or 220 psec.

[0129] The electrical stimulation may be applied for a stimulation period of 0.1 sec-1 month, with periods of rest (i.e., no electrical stimulation) possible in between. In certain cases, the period of electrical stimulation may be 0.1 sec-1 week, 1 sec-1 day, 10 sec-12 hours, 1 min-6 hours, 10 min- 1 hour, and so forth. In certain cases, the period of electrical stimulation may be 1 sec-1 min, 1sec- 30 sec, 1 sec- 15 sec, 1 sec- 10 sec, 1 sec-6 sec, 1 sec-3 sec, 1 sec-2 sec, or 6 sec- 10 sec. The period of rest in between each stimulation period may be 60 sec or less, 30 sec or less, 20 sec or less, or 10 sec. In some embodiments, electrical stimulation may be applied for a year or more, 2 years or more, 3 years or more, 5 years or more, or 10 years or more. In some embodiments, electrical stimulation may be continued indefinitely as part of a long-term DBS therapy regimen.

[0130] The electrical stimulation may be applied with an amplitude of current of 0.1 mA-500 mA, such as, 0.1 mA-25 mA, such as, 0.1 mA-20 mA, 0.1 mA-15 mA, 0.1 mA-10 mA, 0.1 mA-2 mA, 0.1mA-100 mA, 100 mA-200 mA, 200 mA-300 mA, 300 mA-400 mA, or 400 mA-500 mA. In some embodiments, the amplitude of current is 0.1 mA-3.5 mA, or any amplitude of current in this range such as 0.1 mA, 0.2 mA, 0.3 mA, 0.4 mA, 0.5 mA, 0.6 mA, 0.7 mA, 0.8 mA, 0.9 mA, 1.0 mA, 1.1 mA, 1.2 mA, 1.3 mA, 1.4 mA, 1.5 mA, 1.6 mA, 1.7 mA, 1.8 mA. 1.9 mA, 2.0 mA, 2.1 mA, 2.2 mA, 2.3 mA, 2.4 mA, 2.5 mA, 2.6 mA, 2.7 mA, 2.8 mA, 2.9 mA, 3.0 mA, 3.1 mA, 3.2 mA, 3.3 mA, 3.4 mA, or 3.5 mA. In some embodiments, the amplitude of current is 10 mA-500 mA, or any amplitude of current in this range such as 10 mA, 20 mA, 30 mA, 40 mA, 50 mA, 60 mA, 70 mA, 80 mA, 90 mA, 100 mA, 110 mA, 120 mA, 130 mA, 140 mA, 150 mA, 160 mA, 170 mA, 180 mA, 190 mA, 200 mA, 210 mA, 220 mA, 230 mA, 240 mA, 250 mA, 260 mA, 270 mA, 280 mA, 290 mA, 300 mA, 310 mA, 320 mA, 330 mA, 340 mA, 350 mA, 360 mA, 370 mA, 380 mA, 390 mA, 400 mA, 410 mA, 420 mA, 430 mA, 440 mA, 450 mA, or 500 mA. The electrical stimulation may be applied with an amplitude of voltage of 0.1 V-15V, such as, 0.1 V-10V, 0.1 V-5V, 1 V-10V, 1 V-5, V, or 1 V-3.5V. In some embodiments, the amplitude of voltage is 1 V-3.5 V, or any amplitude of voltage in this range such as 1 V, 1.1 , 1.2 V, 1.3 V, 1.4 V, 1.5 V, 1.6 V, 1.7 V, 1.8 V, 1.9 V, 2.0 V, 2.1 V, 2.2 V, 2.3 V, 2.4 V, 2.5 V, 2.6 V, 2.7 V, 2.8 V, 2.9 V, 3.0 V, 3.1 V, 3.2 V, 3.3 V, 3.4 V, or 3.5 V.

[0131] The electrical stimulation having the parameters as set forth above may be applied over a program duration of around 1 day or less, such as, 18 hours, 6 hours, 3 hours, 2 hours, 1 hour, 45 minutes, 30 minutes, 20 minutes, 10 minutes, or 5 minutes, or less, e.g., 1 minute - 5 minutes, 2 minutes - 10 minutes, 2 minutes - 20 minutes, 2 minutes - 30 minutes, 5 minutes - 10 minutes, 5 minutes - 30 minutes, or 5 minutes - 15 minutes, 10 minutes - 400 minutes, 25 minutes - 300 minutes, 50 minutes - 200 minutes, or 75 minutes - 150 minutes, which period would include the application of pulses and the intervening rest period. The program may be repeated at a desired program frequency to ameliorate symptoms of the subject. As such, a treatment regimen may include a program for electrical stimulation at a desired program frequency and program duration. In some embodiments, the treatment regimen is controlled by a control unit in communication with a pulse generator connected to the one or more stimulation electrodes in a closed-loop treatment regimen.

[0132] Alternatively, the subject methods may use transcranial stimulation. The transcranial stimulation may comprise transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation. The parameters for transcranial direct current stimulation may include one or more of current intensity, duration, polarity of the current, current density, ramp time, and the like. The parameters for transcranial alternating current stimulation may include one or more of current amplitude, frequency, phase difference, duration, and the like. The parameters for transcranial magnetic stimulation may include pulse parameters such as intensity,frequency, shape, width, and amplitude; spatial parameters such as coil type and position; and protocol parameters such as train length, inter-train interval, and number of pulses and sessions.

[0133] In some embodiments, transcranial direct current stimulation is applied with a current intensity of 0.5 mA-4 mA such as 0.5 mA-1 mA, 1 mA-2 mA, 1 mA-3 mA, 1.5 mA-3 mA, or 3 mA-4 mA, or any current intensity in these ranges such as 0.5 mA, 1 mA, 1.5 mA, 2 mA, 2.5 mA, 3 mA, 3.5 mA, or 4 mA.

[0134] Polarity of the current refers to the direction of the current flow. The polarity of the transcranial direct current determines whether the current flows into a neuron (anodal) or exits a neuron (cathodal) and the effect on the membrane potential of a neuron. Anodal stimulation typically depolarizes neurons which increases excitability of neurons in the stimulated brain region and increases the number of action potentials. Cathodal stimulation typically hyperpolarizes neurons, which has an inhibitory effect on the stimulated brain region and decreases the number of action potentials.

[0135] Current density refers to the amount of electrical current flowing through a unit area of tissue and is calculated as current intensity divided by the electrode size. In some embodiments, the transcranial direct current density is in a range of 0.1 A / m2to 4 A / m2,0.1 A / m2to 0.5 A / m2, 0.5 A / m2to 1 A / m2, or 1 A / m2to 2 A / m2, including any current density within these ranges such as 0.1 A / m2, 0.2 A / m2, 0.3 A / m2, 0.4 A / m2, 0.5 A / m2, 0.6 A / m2, 0.7 A / m2, 0.8 A / m2. 0.9 A / m2, 1.0 A / m2, 1.1 A / m2, 1.2 A / m2, 1.3 A / m2, 1.4 A / m2, 1.5 A / m2, 1.6 A / m2, 1.7 A / m2, 1.8 A / m2, 1.9 A / m2, 2.0 A / m2, 2.1 A / m2, 2.2 A / m2, 2.3 A / m2, 2.4 A / m2, 2.5 A / m2, 2.6 A / m2, 2.7 A / m2, 2.8 A / m2, 2.9 A / m2, 3.0 A / m2, 3.1 A / m2, 3.2 A / m2, 3.3 A / m2, 3.4 A / m2, 3.5 A / m2, 3.6 A / m2, 3.7 A / m2, 3.8 A / m2, 3.9 A / m2, or 4.0 A / m2.

[0136] In some embodiment, transcranial direct current stimulation having the parameters as set forth above is applied for a duration ranging from 1 minute to 1 hour, including any duration within this range such as 1 minutes, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 11 minutes, 12 minutes, 13 minutes, 14 minutes, 15 minutes, 16 minutes, 17 minutes, 18 minutes, 19 minutes, 20 minutes, 21 minutes, 22 minutes, 23 minutes, 24 minutes, 25 minutes, 26 minutes, 27 minutes, 28 minutes, 29 minutes, 30 minutes, 35 minutes, 40 minutes, 4 minutes 5, 50 minutes, 55 minutes, or 60 minutes.

[0137] In some embodiments, transcranial alternating current stimulation is applied with a current amplitude of 0.5 mA to 2 mA (i.e., measured from the peak of the positive phase to the peak of the negative phase of the sinusoidal wave), including any current amplitude within this range such as 0.5 mA, 0.6 mA, 0.7 mA, 0.8 mA, 0.9 mA, 1.0 mA. 1.1 mA, 1.2 mA, 1.3 mA, 1.4 mA, 1.5 mA, 1.6 mA, 1.7 mA, 1.8 mA, 1.9 mA, or 2.0 mA.

[0138] In some embodiments, transcranial alternating current stimulation is applied with a frequency, ranging from 0.5 Hz to 80 Hz, including any frequency within this range such as 0.5 Hz, 0.6 Hz, 0.7 Hz, 0.8 Hz, 0.9 Hz, 1 Hz, 2 Hz, 3 Hz, 4 Hz, 5 Hz, 6 Hz, 7 Hz, 8 Hz, 9 Hz, 10 Hz, 11 Hz, 12 Hz, 13 Hz, 14 Hz, 15 Hz, 16 Hz, 17 Hz, 18 Hz, 19 Hz, 20 Hz, 25 Hz, 30 Hz, 35 Hz, 40 Hz, 45 Hz, 50 Hz, 55 Hz, 60 Hz, 65 Hz, 70 Hz, 75 Hz, or 80 Hz. In some embodiments, the frequency is chosen to match intrinsic brain oscillations.

[0139] T ranscranial alternating current stimulation uses sinusoidal alternating currents that oscillate back and forth over time. The term “phase difference” refers to the timing difference, measured in degrees, between the oscillating currents applied to different brain regions or electrodes. The choice of phase difference influences the location of the electric field's focus. In some embodiments, transcranial alternating current stimulation is applied with a phase difference of 0°, 45°, 60°, 90°, or 180°, wherein 360° represents a full cycle of a wave. For example, a phase difference of 0° (in- phase) can be used to synchronize neural oscillations in different brain areas, whereas a phase difference of 180° (anti-phase) can be used to desynchronize neural oscillations in different brain areas.

[0140] In some embodiments, transcranial alternating current stimulation having the parameters as set forth above is applied for a duration ranging from 1 minute to 24 hours, 1 minute to 10 hours, 1 minute to 1 hour, or 1 hour to 5 hours, including any duration within these ranges such as 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes, 30 minutes, 35 minutes, 40 minutes, 45 minutes, 50 minutes, 55 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11, hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, or 24 hours.

[0141] The puise intensity used in transcranial magnetic stimulation refers to the strength of the magnetic pulse, which is typically expressed as a percentage of an individual's motor threshold (MT). In some embodiments, transcranial magnetic stimulation is delivered at 80% to 120% of the MT of a subject, including any percentage of MT in this range such as 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 110%, 111%, 112%, 113%, 114%, 115%, 116%, 117%, 118%, 119%, or 120%.

[0142] In some embodiments, transcranial magnetic stimulation is delivered with a pulse frequency in a range of 0.5 Hz to 1000 Hz, 0.5 Hz to 25 Hz, 0.5 Hz to 1 Hz, 5 Hz to 10 Hz, 10 Hz to 20 Hz, 15 Hz to 25 Hz, or 50 Hz to 1000 Hz, including any pulse frequency within these ranges such as 0.5 Hz, 1 Hz, 2 Hz, 3 Hz, 4 Hz, 5 Hz, 6 Hz, 7 Hz, 8 Hz, 9 Hz, 10 Hz, 11 Hz, 12 Hz, 13 Hz, 14 Hz, 15 Hz, 16Hz, 17 Hz, 18 Hz, 19 Hz, or 20 Hz. Pulse frequencies of 1 Hz or less (i.e., low frequency) reduce cortical excitability whereas higher pulse frequencies, typically, in a range of 5 Hz to 20 Hz (i.e., high frequency) increase cortical excitability, induce synaptic plasticity, and strengthen neuronal connections in the brain. Even higher pulse frequencies such as in a range of 50 Hz to 1000 Hz (i.e., ultra-high frequency) are generally excitatory and may be used to increase brain activity.

[0143] The stimulus train duration is the length of time of a burst or train of magnetic pulses. In some embodiments, transcranial magnetic stimulation is applied with a stimulus train duration ranging from 2 to 5 seconds, including any stimulus train duration within this range such as 2.0 seconds, 2.1 seconds, 2.2 seconds, 2.3 seconds, 2.4 seconds, 2.5 seconds, 2.6 seconds, 2.7 seconds, 2.8 seconds, 2.9 seconds, 3.0 seconds, 3.1 seconds, 3.2 seconds, 3.3 seconds, 3.4 seconds, 3.5 seconds, 3.6 seconds, 3.7 seconds, 3.8 seconds, 3.9 seconds, 4.0 seconds, 4.1 seconds, 4.2 seconds, 4.3 seconds, 4.4 seconds, 4.5 seconds, 4.6 seconds, 4.7 seconds, 4.8 seconds, 4.9 seconds, or 5.0 seconds.

[0144] The inter-train interval is the time between different bursts or trains of pulses. In some embodiments, transcranial magnetic stimulation is applied with an inter-train interval ranging from 1 second to 1 minute, 2 seconds to 10 seconds, 10 seconds to 20 seconds, 20 seconds to 30 seconds, or 40 seconds to 50 seconds, including any inter-train interval within these ranges such as 1 second, 2 seconds, 3 seconds, 4 seconds, 5 seconds, 6 seconds, 7 seconds, 8 seconds, 9 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, or 50 seconds.

[0145] In some embodiments, transcranial magnetic stimulation is applied with a total number of pulses per session ranging from 500 pulses to 100,000 pulses, 2000 pulses to 4500 pulses, 5000 pulses to10,000 pulses, 20,000 pulses to 50,000 pulses, or 50,000 pulses to 100,000 pulses, including any number of pulses within these ranges such as 500 pulses, 600 pulses, 700 pulses, 800 pulses, 900 pulses, 1000 pulses, 1100 pulses, 1200 pulses, 1300 pulses, 1400 pulses, 1500 pulses, 1600 pulses, 1700 pulses, 1800 pulses, 1900 pulses, 2000 pulses, 2100 pulses, 2200 pulses, 2300 pulses, 2400 pulses, 2500 pulses, 2600 pulses, 2700 pulses, 2800 pulses, 2900 pulses, 3000 pulses, 3500 pulses, 4000 pulses, 4500 pulses, 5000 pulses, 5500 pulses, 6000 pulses, 6500 pulses, 7000 pulses, 7500 pulses, 8000 pulses, 8500 pulses, 9000 pulses, 9500 pulses, 10,000 pulses, 20,000 pulses, 30,000 pulses, 40,000 pulses, 45,000 pulses, 50,000 pulses, 55,000 pulses, 60,000 pulses, 65,000 pulses, 70,000 pulses, 75,000 pulses, 80,000 pulses, 85,000 pulses, 90,000 pulses, 95,000 pulses, or 100,000 pulses.

[0146] The pulse shape refers to the waveform of the magnetic pulse. The waveform can be monophasic (unidirectional), biphasic (bidirectional, with two phases), or polyphasic (multi-phase).In some embodiments, transcranial magnetic stimulation is applied with a sinusoidal, square, rectangular, or customized wave pulse shape.

[0147] Alternatively, the subject methods may use ultrasound stimulation. The parameters for ultrasound stimulation may include one or more of stimulation duration, ultrasound frequency, pulse repetition frequency, number of pulses, pulse length, duty cycle, negative peak pressure, spatial average pulse average intensity (Isapa), and the like. The ultrasound stimulation may comprise pulsed focused ultrasound, including low-intensity pulsed ultrasound (LI PUS) or high-intensity pulsed ultrasound (HIFU).

[0148] In certain embodiments, pulsed focused ultrasound has an ultrasound frequency ranging from about 20 kHz to about 5.0 MHz, about 0.7 MHz to about 3.0 MHz, or about 1.0 MHz to about 1.1 MHz, including any ultrasound frequency within these ranges, such as 0.2 MHz, 0.4 MHz, 0.6 MHz, 0.8 MHz, 1.0 MHz, 1.1 MHz, 1.2 MHz, 1.3 MHz, 1.4 MHz, 1.5 MHz, 1.6 MHz, 1.7 MHz, 1.8 MHz, 1.9 MHz, 2.0 MHz, 2.1 MHz, 2.2 MHz, 2.3 MHz, 2.4 MHz, 2.5 MHz, 2.6 MHz, 2.7 MHz, 2.8 MHz, 3.0 MHz, 3.2 MHz, 3.4 MHz, 3.6 MHz, 3.8 MHz, 4.0 MHz, 4.2 MHz, 4.4 MHz, 4.6 MHz, 4.8 MHz, or 5.0 MHz.

[0149] In certain embodiments, the pulsed focused ultrasound has a pulse repetition frequency (PRE) ranging from 0.1 Hz to 1000 Hz, 1 Hz to 100 Hz, or about 5 Hz to 20 Hz, or any PRE with these ranges, such as 0.1 Hz, 0.2 Hz, 0.3 Hz, 0.4 Hz, 0.5 Hz, 0.6 Hz, 0.7 Hz, 0.8 Hz, 0.9 Hz, 1 Hz, 2 Hz, 3 Hz, 4 Hz, 5 Hz, 6 Hz, 7 Hz, 8 Hz, 9 Hz, 10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz, 80 Hz, 90 Hz, 100 Hz, 150 Hz, 200 Hz, 250 Hz, 300 Hz, 350 Hz, 400 Hz, 450 Hz, 500 Hz, 550 Hz, 600 Hz, 650 Hz, 700 Hz, 750 Hz, 800 Hz, 850 Hz, 900 Hz, 950 Hz, or 1000 Hz.

[0150] In certain embodiments, the pulsed focused ultrasound has a duty cycle ranging from 0.01% to 100% or 1% to 20%, including any ultrasound duty cycle within these ranges such as 0.01%, 0.1%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 90%, 95%, or 100%.

[0151] In certain embodiments, the pulsed focused ultrasound has a negative peak pressure (NPP) ranging from 0.1 MPa to 10 MPa, including any NPP within this range such as 0.1 MPa, 0.2 MPa, 0.3 MPa, 0.4 MPa, 0.5 MPa, 0.6 MPa, 0.7 MPa, 0.8 MPa, 0.9 MPa, 1 MPa, 2 MPa, 3 MPa, 4 MPa, 5 MPa, 6 MPa, 7 MPa, 8 MPa, 9 MPa, or 10 MPa. In some embodiments, the pulsed focused ultrasound is administered with a negative peak pressure (NPP) of up to 3 MPa. In some embodiments, the pulsed focused ultrasound is administered with a negative peak pressure (NPP) of up to 3 MPa.

[0152] In certain embodiments, the pulsed focused ultrasound is administered to the subject for a time ranging from about 20 seconds to about 7 minutes, including any amount of time within thisrange, such as 20 seconds, 25 seconds, 30 seconds, 35 seconds, 40 seconds, 45 seconds, 50 seconds, 55 seconds, 1 minute, 1.25 minutes, 1.5 minutes, 1.75 minutes, 2 minutes, 2.25 minutes, 2.5 minutes, 2.75 minutes, 3 minutes, 3.25 minutes, 3.5 minutes, 3.75 minutes, 4 minutes, 4.25 minutes, 4.5 minutes, 4.75 minutes, 5 minutes, 5.25 minutes,, 5.5 minutes, 5.75 minutes, 6 minutes, 6.25 minutes, 6.5 minutes, 6.75 minutes, or 7 minutes. In some embodiments, the pulsed focused ultrasound is administered to the subject for at least 20 seconds. In some embodiments, the pulsed focused ultrasound is administered to the subject for a period ranging from about 1 minute to about 5 minutes.

[0153] As noted above, the treatment may ameliorate one or more symptoms of a neurological or psychiatric disorder suffered by the subject. In the case of a movement disorder, the treatment may assist movement of the subject and reduce motor symptoms such as bradykinesia, dyskinesia, dysarthria, dystonia, tremor, or gait disturbance. Assessment of effectiveness of the treatment may be performed using any known method for evaluating motor symptoms. In some embodiments, efficacy of the treatment is evaluated using a Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) or a Hoehn and Yahr (HnY) scale, a Parkinson’s Disease Composite Scale (PDCS), or a Schwab and England Activities of Daily Living (ADL) Scale. In some embodiments, assessing effectiveness of the treatment of a movement disorder comprises monitoring the subject using multi-view video recordings of the subject, keylogging data from a computer used by the subject, or a wearable monitor that can acquire accelerometry, gyroscope and / or surface electromyographic (sEMG) data. For example, a wristwatch style wearable monitor such as the Parkinson’s KinetiGraph®, PKG®, available from PKG Health (San Francisco, CA), can be used to monitor movement continuously to detect various motor symptoms of a movement disorder such as bradykinesia, dyskinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation. An Apple Watch is available from Apple Inc. (Cupertino, CA), which can be used, for example, for monitoring, bradykinesia, dyskinesia, tremors, gait, and arm movement.

[0154] In the case of a psychiatric disorder, the symptoms may include anxiety, depression, frequency of episodes of compulsive behavior, frequency of self-starvation episodes, mania, panic attacks, social anxiety, or distress related to chronic pain. Assessment of effectiveness of the treatment may be performed by neurological examinations and / or neuropsychological tests (e.g., Minnesota Multiphasic Personality Inventory, Beck Depression Inventory, Mini-Mental Status Examination (MMSE), Visual Analogue Scale for Depression (VAS-D), Depression Rating Scale (HAM-D), Montgomery-Asberg Depression Rating Scale (MADRS), Wisconsin Card Sorting Test (WCST), Tower of London, Stroop task, Montgomery-Asberg Depression Rating Scale (MADRS), aHamilton Depression Rating Scale (HDRS), Zung Self-Rating Depression Scale (SDS), Yale-Brown Obsessive Compulsive score (Y-BOCS), a Bipolar Affective Disorder Dimension Scale (BADDS), a Mood Disorder Questionnaire (MDQ), a Patient Health Questionnaire-9 (PHQ-9)), motor examination, visual analog scales of pain symptoms, and / or cranial nerve examination.

[0155] In certain cases, the symptom that may be ameliorated by the disclosed method may be anxiety. Anxiety of a subject may be assessed using any standardized assessment method. In certain cases, anxiety may be measured by self-reporting, such as, by a Beck Anxiety Inventory (BAI) score, Visual Analogue Scale for Anxiety (VAS-A), or Hamilton Anxiety Rating Scale (HAM-A). Additional measures of anxiety include State-straight anxiety inventory (STAI) which consists of State Anxiety Scale (S-Anxiety) and Trait Anxiety Scale (T-Anxiety) and hospital anxiety and depression scale-Anxiety (HADS-A). Amelioration of anxiety may include a reduction in anxiety level compared to the anxiety level prior to the treatment. A 5% or higher reduction in anxiety level (measured by BAI or HAM-A, for example), may indicate that the treatment was effective.

[0156] In certain cases, the symptom that may be ameliorated by the disclosed method may be depression. Depression of a subject may be assessed using any standardized assessment method. In certain cases, depression may be measured by self-reporting, such as, by Visual Analogue Scale for Depression (VAS-D), Hamilton Depression Rating Scale (HAM-D), Montgomery-Asberg Depression Rating Scale (MADRS), a Beck Depression Inventory (BDI) score, Center for Epidemiological Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), or Zung Self-Rating Depression Scale (Zung SDS). In certain cases, an interviewer-administered depression assessment may be used alone or in conjunction with a self-reporting tool. Interviewer-administered depression assessments include Cornell Scale for Depression in Dementia (CSDD) and RAND Corporation Self-Administered Depression Screener Amelioration of depression may include a reduction in depression level compared to the depression level prior to the treatment. A 5% or higher reduction in depression level (measured by BDI score, for example), may indicate that the treatment was effective.

[0157] In certain cases, the symptom that may be ameliorated by the disclosed method is a symptom of a sleep disorder such as insomnia, hyposomnia, or hypersomnia. Symptoms of a sleep disorder may be assessed using any standardized assessment method. In certain cases, the symptom of the sleep disorder is measured by self-reporting, such as, by using a visual-analog scale (VAS), a psychomotor vigilance test (PVT), a Likert scale, a Stanford Sleepiness Scale (SSS), a maintenance of wakefulness test (MWT), an Epworth sleepiness scale (ESS), a multiple sleep latency test (MSLT), or an Athens insomnia scale (AIS). In some cases, the symptom of the sleep disorder is measured with a polysomnogram.

[0158] In certain cases, the symptom that may be ameliorated by the disclosed method is pain. Assessment of effectiveness of the treatment may be performed using any known method for evaluating pain. In certain cases, an interviewer-administered pain assessment may be used alone or in conjunction with a self-reporting tool. Interviewer-administered pain assessments may use a numerical rating scale (NRS), verbal descriptor scale (VDS), visual analog scale (VAS), or a verbal rating scale. In some cases, pain is assessed using a McGill Pain Questionnaire (MPQ), a Wong- Baker FACES® scale, or a behavioral observation scale such as the Face, Legs, Activity, Cry, Consolability (FLACC) scale or behavioral pain scale (BPS).

[0159] In certain cases, effectiveness of treatment may be assessed by detecting brain electrical activity (e.g., neural signals from neural activity associated with a symptom) in a brain region, which will depend on the particular disorder being treated. For example, the brain region may be a sensorimotor cortex region, central sulcus region, precentral gyrus region, postcentral gyrus region, amygdala region, orbitofrontal cortex region, hippocampus region, septum region, cingulate gyrus region, cingulate cortex region, subgenual cingulate region, hypothalamus region, epithalamus region, anterior thalamus region, mammillary body region, or fornix region. Detection of brain activity may be performed by functional brain imaging. Functional brain imaging may be carried out by electrical methods such as electroencephalography (EEG), chronic subgaleal recordings, burrhole or cranially mounted neurostimulator electrode recording, electrocorticography (ECoG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), as well as metabolic and blood flow studies such as functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). In some embodiments, electrical methods for assessing effectiveness of treatment may involve use of a neural recording electrode as described herein or placement of an additional electrode for measuring electrical signals at a secondary region of the brain or in the skull, or extracranially. One or more regions of the brain may be implanted with an electrode and electrical signals measured for assessment of effectiveness of the treatment. Any suitable electrodes may be used for measurements and may include one or more surface electrodes (non-brain penetrating electrode(s)) or one or more depth electrodes (brain penetrating electrode(s)) as described herein.

[0160] Assessment of effectiveness of treatment may be performed at any suitable time point after commencement of the treatment procedure, for example, during open-loop or closed-loop therapy. Embodiments of the subject methods include assessing effectiveness of neurostimulation in treating a symptom of a neurological disorder or psychiatric disorder in a subject within seconds, minutes, hours, or days after the initial treatment regimen has been completed. In some instances, assessment may be performed at multiple time points. In some cases, more than one type ofassessment may be performed at the different time points. In some embodiments, brain activity including brain electrical signals from neural activity associated with symptom may be measured prior to the application of neurostimulation, and assessing may include comparing the subject’s brain activity after the treatment to that before the treatment and a change in the post-treatment brain activity may indicate successful treatment.

[0161] Upon completion of a treatment regimen, the patient may be assessed for effectiveness of the treatment and the treatment regimen may be repeated, if needed. In certain cases, the treatment regimen may be altered before repeating. For example, one or more of the stimulation parameters such as, but not limited to, frequency, pulse width, pulse shape, pulse repetition frequency, number of pulses, current amplitude, current polarity, current density, phase difference, duty cycle, stimulation duration, program duration, program frequency, and / or placement of a stimulation electrode, neural recording electrode, magnetic coil, or pulse generator / stimulator device may be altered before starting a second treatment regimen.

[0162] Application of the method may include a prior step of selecting a patient for treatment based on need as determined by clinical assessment, which may include assessment of severity of the neurological disorder or psychiatric disorder, physical condition, cognitive assessment, anatomical assessment, behavioral assessment and / or neurophysiological assessment. In certain cases, a subject may be further assessed to determine if adaptive neurostimulation will completely or partially (e.g., at least 50%) relieve symptoms. Such a patient may undergo neurostimulation therapy on a temporary trial basis to determine if neurostimulation decreases the severity of symptoms experienced by the patient. Such a patient may also be implanted with neural recording electrodes to identify personalized neural signatures of “symptoms” and “relief of symptoms” to assist with stimulation programming to determine therapeutic stimulation parameters for the patient and / or evaluate whether neurostimulation therapy will be effective for treating the neurological disorder or psychiatric disorder in the patient.

[0163] In some cases, data driven approaches are used to identify spectral features that are individualized and different from canonical power bands. In some cases, the application of stimulation may alter other neural features from one more regions of the brain. The alterations may be compared to the state of these features prior to the application of stimulation.

[0164] A closed-loop method allows determination of parameters of neurostimulation based upon real-time feedback signals from the brain of the subject. Closed-loop methods and systems allow for automation of treatment of the subject including real-time need-based modulation of the treatment regimen. Volitional self-modulation of neurostimulation using a brain computer interface to provide neurofeedback to a patient, as described herein, may be used in combination with a closed-loopneurostimulation system for treatment of a neurological disorder or psychiatric disorder. In some embodiments, a weighted combination of classic closed-loop adaptive neurostimulation plus a secondary volitional self-modulation mechanism is used for neurostimulation. Certain disorders may inherently lend themselves to this method more effectively than others, potentially due to the neurophysiology of the symptoms or the patient’s capacity for cognitive engagement. Exemplary systems using a brain computer interface to provide neurofeedback to a patient to allow volitional self-modulation of neurostimulation are further discussed in the Examples section and are depicted in FIGS. 1 and 6B.Systems and Computer Implemented Methods for Volitional Self-Modulation of Neurostimulation

[0165] The present disclosure also provides systems and computer implemented methods which find use in practicing the subject methods. The system may be an open-loop or closed-loop system configured for performing the methods provided herein. In some embodiments, the system comprises a stimulator (e.g., capable of providing electrical stimulation, magnetic stimulation, or ultrasound stimulation), and a neural recording electrode, adapted for positioning at a region (e.g., sensorimotor cortex region, subthalamic nucleus region, or globus pallidus internus region for detecting neural signals associated with a symptom of a movement disorder such as Parkinson’s disease; a right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, or right hippocampus region for detecting neural signals associated with a symptom of a psychiatric disorder; or a frontal lobe region for detecting neural signals associated with pain) of the brain of the subject to record brain electrical signal data before, during, or after stimulation is delivered to the subject with a stimulator. Various types of stimulators may be included in the system, including stimulators that provide deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation (e.g., transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation), spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation (e.g., using ultrasound transducer).

[0166] In certain embodiments, the system further comprises a brain computer interface (BCI) connected to the neural recording electrode and a data receiving device, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to the data receiving device, wherein the data receiving device comprises a processor coupled to a display. To provide neurofeedback, the display displays a graphical object, wherein the magnitude of a recorded neural signal is represented as a vertical position (i.e. , height) of thegraphical object on the display. The settings of the stimulator can be adjusted by the subject based on using the BCI to adjust the vertical position of the graphical object displayed on the display by modulating the magnitude of the recorded neural signal, wherein the subject voluntarily increases or decreases the amplitude of the stimulation delivered to the brain using the vertical position of the graphical object on the display as a guide. The processor is programmed to instruct a stimulator to apply stimulation using the adjusted settings determined by the subject using the neurofeedback provided through the BCI to treat a symptom of the neurological disorder or psychiatric disorder of the subject. In some embodiments, the user interface is password protected and is operable by a health care practitioner.

[0167] In some embodiments, the system further comprises a stimulation electrode adapted for positioning at a location in a brain region (e.g., a subthalamic nucleus region or a globus pallidus internus region for treatment of a movement disorder such as Parkinson’s disease; or a ventral capsule / ventral striatum region, subgenual cingulate region, or orbitofrontal cortex region for treatment of a psychiatric disorder) of a subject to deliver electrical stimulation.

[0168] In some embodiments, the system further comprises a stimulation electrode adapted for positioning in an epidural space for delivering spinal cord stimulation to a subject. The stimulation electrode may be placed, for example, in the epidural space between vertebrae T9 to T10 to relieve lower back pain, at lower thoracic levels for lower limb pain, and at cervical levels for upper limb pain.

[0169] In some embodiments, the system further comprises a stimulation electrode adapted for positioning at a location near a nerve for delivering peripheral nerve stimulation or peripheral nerve field stimulation. The stimulation electrode may be placed, for example, near a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve for delivering peripheral nerve stimulation to a subject. Stimulation electrodes may be placed near small nerve fibers under the skin in an area of pain to create an electrical field around the small nerve fibers for delivering peripheral nerve field stimulation to a subject.

[0170] In some embodiments, the system further comprises a magnetic coil for delivering transcranial magnetic stimulation to a subject. The magnetic coil is placed on the scalp of a subject to deliver magnetic pulses to targeted brain areas.

[0171] In some embodiments, the system further comprises a wearable monitor that can acquire accelerometry data, gyroscope data, magnetometer surface electromyographic (sEMG) data, or any combination thereof to detect symptoms of the neurological disorder or psychiatric disorder such as,but not limited to, motor symptoms such as dyskinesia, bradykinesia, tremor, daytime immobility, stiffness, slow movements, gait / walking, daytime somnolence, and sleep fragmentation. Accelerometry, gyroscope, and / or sEMG data from a wearable monitor can be combined with brain electrical signal data to assist symptom classification.

[0172] In some embodiments, a computer implemented method is used to provide neurofeedback to assist a subject to volitionally control stimulation for treatment of a neurological disorder or psychiatric disorder. A processor can be programmed to perform steps of a computer implemented method comprising: receiving recorded neural signal data from a neural recording electrode positioned at a first location in or near a brain region of the subject, wherein the neural recording electrode is connected to a BCI, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to a data receiving device comprising a processor coupled to a display; displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data; adjusting settings of a stimulator based on the subject voluntarily increasing or decreasing the amplitude of the stimulation delivered to the subject by adjusting the vertical positioning of the graphical object on the display; and instructing the stimulator to deliver the stimulation to the subject using the adjusted settings, wherein the stimulation is delivered to a second location in the subject. In some embodiments, the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal. In some embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

[0173] The graphical object can have any suitable form. In certain embodiments, the graphical object resembles a flying or floating object such as, but not limited to, an airplane, helicopter, spaceship, drone, kite, balloon, bird, or butterfly. In some embodiments, multiple forms of the graphical object may be provided to allow a subject to select from multiple options. In certain embodiments, the display further displays a background comprising features moving from right to left (e.g., to simulate movement of the graphical object horizontally across the background). In some embodiments, the background comprises background graphics resembling a landscape comprising scenery, which may include, for example, without limitation, sky with a horizon over land or ocean. In some embodiments, the horizontal speed of the graphical object is constant.

[0174] In certain embodiments, the computer-implemented method further comprises providing training to the subject on how to volitionally control the amplitude of the stimulation. The display further displays a target having a target shape, wherein training comprises having the subject adjust the amplitude of the stimulation by controlling the magnitude of the neural signal through adjustingthe vertical positioning of the graphical object to hit the target on the display. The target may have any suitable shape. In some embodiments, the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.

[0175] In some embodiments, the target is positioned at a position between 25% to 75% of the height of the display, including any position within this range such as 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60, % 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, or 75% of the height of the display. In some embodiments, the target is positioned at 25% or 75% of the height of the display.

[0176] In some embodiments, the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal. For example, if the neural signal is a cortical beta signal (e.g., oscillations in a range of 15 Hz to 20 Hz), the vertical position of the graphical object on the display would be proportional to log-transformed cortical beta power. In other embodiments, the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal. For example, if the neural signal is a cortical beta signal (e.g., oscillations in a range of 15 Hz to 20 Hz), the vertical position of the graphical object on the display would be inversely proportional to log-transformed cortical beta power. In some embodiments, the vertical position of the graphical object on the display is controlled by the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.

[0177] In certain embodiments, the computer-implemented method further comprises automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject. In some embodiments, the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.

[0178] In certain embodiments, the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model. In some embodiments, the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output. In some embodiments, the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal. In some embodiments, the machine learning model uses multivariate analysis of the neural signal data. In some embodiments, the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection. In some embodiments, the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination. In some embodiments, the machine learning model uses dimension decomposition to isolate the biomarkerfrom a confound. In some embodiments, the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof. In some embodiments, the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker. In some embodiments, the machine learning model is further used to distinguish movement artifacts from the neural signal. In some embodiments, the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data. In some embodiments, the machine learning model uses reinforcement learning threshold adaptation. In some embodiments, the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.

[0179] After providing the training to the subject, in some embodiments, the computer-implemented method further comprises instructing the stimulator to deliver stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.

[0180] In certain embodiments, the computer-implemented method further comprises: displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; and subsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time. In some embodiments, the computer- implemented method further comprises instructing the stimulator to deliver the stimulation to the subject using settings adjusted by the subject without displaying the graphical object on the display based on the training to achieve the learned neural state. In some embodiments, the neural state is an induced neuroplasticity state or a patient-generated neural state. In some embodiments, the stimulation induces the neuroplasticity state or the patient-generated neural state. In some embodiments, the computer-implemented method further comprises triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state. Exemplary assistive devices include, but are not limited to, an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, and a language production device. In some embodiments, the computer-implemented method further comprises delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device. In some embodiments, the computer-implemented method further comprises: delivering transcranialstimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

[0181] In certain embodiments, the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.

[0182] In certain embodiments, the computer-implemented method further comprises: instructing a video recording device to record a video of the subject during the training; and analyzing the video to determine if the subject remained immobile during the training.

[0183] In certain embodiments, the computer-implemented method further comprises: setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.

[0184] The methods can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, a data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine- readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or any combination thereof.

[0185] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0186] In a further aspect, the system for performing the computer implemented method, as described, may include a processor, a storage component (i.e., memory), a display component, and other components typically present in general purpose computers. In some embodiments, the processor is provided by a computer or handheld device (e.g., a cell phone or tablet). The storage component stores information accessible by the processor, including instructions that may be executed by the processor and data that may be retrieved, manipulated or stored by the processor.

[0187] The storage component includes instructions. For example, the storage component includes instructions for providing neurofeedback that can be used by a subject to volitionally control brain electrical stimulation according to the methods described herein. The computer processor is coupled to the storage component and configured to execute the instructions stored in the storage component in order to receive recorded neural signal data from a neural recording electrode connected to a BCI, and display a graphical object on a display to provide neurofeedback, wherein vertical positioning of the graphical object on the display is controlled by the magnitude of a neural signal (see, e.g., Examples).

[0188] The processor and / or memory may be operably connected to a display device, for example, via a wired, such as a Universal Serial Bus (USB) connection, or wireless connection, such as a Bluetooth connection. Any convenient display device, such as a liquid crystal display (LCD), lightemitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device may be used. The display component displays the graphical object, wherein vertical positioning of the graphical object on the display is controlled by magnitude of the neural signals recorded by the neural recording electrode. In certain embodiments, the display further displays a target or multiple targets for training the subject. In certain embodiments, the display further displays a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background.

[0189] The storage component may be of any type capable of storing information accessible by the processor, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, USB Flash drive, write- capable, and read-only memories. The processor may be a general purpose processor, a graphics processor unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor can also include primarily analog components. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a graphics processor unit, a mainframecomputer, a digital signal processor, a portable computing device, a personal organizer, a device controller, and a computational engine within an appliance, to name a few.

[0190] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module, engine, and associated databases can reside in memory resources such as in RAM memory, FRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of non-transitory computer-readable storage medium, media, or physical computer storage known in the art. An exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0191] The instructions may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. In that regard, the terms "instructions," "steps" and "programs" may be used interchangeably herein. The instructions may be stored in object code form for direct processing by the processor, or in any other computer language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance.

[0192] Data may be retrieved, stored or modified by the processor in accordance with the instructions. For instance, although the system is not limited by any particular data structure, the data may be stored in computer registers, in a relational database as a table having a plurality of different fields and records, XML documents, or flat files. The data may also be formatted in any computer-readable format such as, but not limited to, binary values, ASCII or Unicode. Moreover, the data may comprise any information sufficient to identify the relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (including other network locations) or information which is used by a function to calculate the relevant data.

[0193] In certain embodiments, the processor and storage component may comprise multiple processors and storage components that may or may not be stored within the same physical housing. For example, some of the instructions and data may be stored on removable CD-ROM and others within a read-only computer chip. Some or all of the instructions and data may be stored in a location physically remote from, yet still accessible by, the processor. Similarly, the processor may comprise a collection of processors which may or may not operate in parallel.

[0194] In some embodiments, the method can be performed using a cloud computing system. In these embodiments, the neural signal data comprising recorded neural signals associated with a symptom of the neurological disorder or the psychiatric disorder and the programming can be exported to a cloud computer, which runs the program, and returns an output to the user.Closed-Loop Method for Automated Delivery of Stimulation

[0195] A control algorithm may be used to automate the delivery of stimulation (e.g., electrical stimulation, magnetic stimulation, or ultrasound stimulation) to the subject in response to detection of neural activity associated with a symptom of a neurological disorder or a psychiatric disorder with stimulator settings adjusted by a subject using neurofeedback provided through a BCI, as described herein (see Examples). According to certain embodiments, the method may include measuring neural signals associated with a symptom of a neurological disorder or a psychiatric disorder from a region (e.g., cortical precentral gyrus region, postcentral gyrus region, subthalamic nucleus region, or a globus pallidus internus region for treatment of a movement disorder such as Parkinson’s disease; a right amygdala region, left amygdala region, right orbitofrontal cortex region, left subgenual cingulate region, right hippocampus region for treatment of a psychiatric disorder; or a frontal lobe region for treatment of pain) of the brain of the subject via a neural recording electrode; applying electrical signal metrics to a control algorithm that is tuned to a clinically relevant target (e.g., a range of signal indicative of effective treatment of the symptom of the neurological disorder or the psychiatric disorder); and automatically delivering stimulation (e.g., electrical stimulation, magnetic stimulation, or ultrasound stimulation) using a stimulator in a manner effective to treat the symptom if the electrical signal metrics indicate that the patient is in need of treatment. For example, the levels of overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta) may be measured with a neural recording electrode, wherein the control algorithm receives the electrical activity data from the neural recording electrode and automates delivery of stimulation using the stimulator when the level of or power in a specific frequency range indicates that the patient is having the symptom. In some embodiments, one or more programmed stimulation parameters are modulated by a subject using neurofeedback provided through a BCI; and modulated stimulation is delivered to subject in a manner effective to mitigate the symptom of the subject.

[0196] As described in the foregoing sections, effectiveness of treatment of a symptom of a neurological disorder or psychiatric disorder may be assessed by detecting neural signals associated with the symptom using a neural recording electrode. In an open-loop system, stimulation is delivered in a pre-programmed way or manually by a user but is not automatically controlled by real-time neural feedback from the patient’s brain. In a closed-loop system, by contrast, a computing means canautomatically update stimulation parameters based upon analysis of the recorded neural signals and / or automatically deliver stimulation to the subject with stimulator parameter settings adjusted by the subject using neurofeedback provided through a BCI. In some embodiments, either an openloop or a closed-loop system may be integrated with a mechanism for user intervention, for example by allowing user-override of open-loop or closed-loop stimulation programs to enact or prevent stimulation that would ordinarily occur, or to manually change parameters of such stimulation.

[0197] In some embodiments, the computing means for directing closed-loop stimulation may be a combination of hardware / software which may be connected wirelessly or by wire to the neural recording electrodes. The computing means may communicate with a control unit (also referred to as a control module) that controls a stimulator (e.g., a neurostimulator pulse generator connected to stimulation electrodes for deep brain stimulation, peripheral nerve stimulation, spinal cord electrical stimulation, or transcranial direct current or transcranial alternating current stimulation; a neurostimulator connected to a magnetic coil for transcranial magnetic stimulation; an ultrasound transducer for ultrasound stimulation). In certain embodiments, the computing means may be connected to a recorder (e.g., a neurophysiological recorder or neural recording device) that records brain activity measured by the neural recording electrodes. In some embodiments, a control algorithm is used, which operates by simple on / off control of stimulation at set parameters, modifying only the on / off parameter with each evaluation cycle. In some cases, the algorithm may be based on information related to the symptom of the neurological disorder or psychiatric disorder, such as, a range of electrical activity (e.g., alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations) that is indicative of the symptom to be treated with stimulation. The algorithm may also include additional information such as a brain activity profile of a normal subject (not suffering from the symptom). Regardless of the particular control algorithm structure, the computing means may be tuned to a clinically relevant target (e.g., a range of signal indicative of effective treatment and / or a range of signal indicative of presence of the symptom and the need for treatment) that directs modulation of one or more programmed stimulation parameters according to the algorithm’s control law, applying the modulated stimulation to the subject via the stimulator. In some embodiments, the subject undergoing treatment with stimulation adjusts the stimulation amplitude using neurofeedback provided through a BCI, as described herein.

[0198] In some cases, the computing means, via a control algorithm, may determine whether the received electrical signals are within or outside a predetermined range of neural signals indicative of the presence of the symptom targeted for treatment with electrical stimulation. When the received electrical signals are outside this predetermined range, then the computing means determines that the symptom is absent. The computing means may then communicate with the control unit to directstimulation shut-off by the stimulator. When the received electrical signals are within the predetermined range of neural signals indicative of the targeted symptom, then the computing means determines that the subject should be treated with stimulation. The control algorithm within the computing means may then determine whether the initial step of applying stimulation to the subject should be repeated and / or whether a parameter of the stimulation should be modified prior to the step of applying stimulation when brain activity indicating the presence of the symptom is detected. The computing means, via the control unit, may then communicate with the control unit to provide the appropriate instructions to the stimulator.

[0199] In some embodiments, the computing means may determine whether the received electrical signals are within or outside a second predetermined range, where the second predetermined range is indicative of a second symptom targeted for treatment with stimulation. When the received electrical signals are within the second predetermined range, then the computing means determines that the subject should be treated with stimulation. The computing means may then communicate with the control unit to direct stimulation switch-off by the stimulator when the received electrical signals are outside the second predetermined range. The control algorithm within the computing means may then determine whether the initial step of applying stimulation should be repeated and / or whether a parameter of the stimulation modified prior to the step of applying stimulation. The processor may then communicate with the control unit to provide the appropriate instructions to the stimulator.

[0200] Thus, in certain aspects, the subject methods operate as a closed-loop control system which may automatically adjust one or more parameters in response to electrical activity from a region of the brain of a subject and / or automatically deliver stimulation to the subject according to the stimulation program. In some embodiments, the closed-loop control system automatically delivers stimulation according to set parameters when the received electrical signals are within a predetermined range indicative of a symptom targeted for treatment with stimulation.

[0201] In some aspects, the closed loop system may be used to sense a subject’s need for treatment using the methods disclosed herein. For example, the closed loop system may be programmed to monitor brain activity from one or more regions of the brain and compare the brain activity corresponding to a symptom to a range indicative of the presence of the symptom. Upon detection of electrical activity indicative of the symptom, the closed loop system may automatically commence a treatment protocol of applying stimulation to the subject to target the symptom.

[0202] It is understood that electrical signals that are indicative of a symptom or relief of a symptom for a subject may be recorded from a subject’s brain and may be used in aspects outside of a closed loop system. For example, electrical signals indicative of a symptom or relief of a symptom for asubject may be recorded using electrodes or another device operably coupled to the patient’s brain, which electrodes or device may or may not be part of a closed loop system. The patient may be treated as disclosed herein (e.g., by applying stimulation to the subject), and electrical signals may be recorded in real time as the treatment is administered or after the treatment is administered. The electric signals recorded after the administration of stimulation is commenced may then be compared to the electric signals recorded prior to the treatment to determine features in the recorded electric signals that change post-treatment. These features provide a feedback signal to indicate whether the treatment is having an effect on the patient’s symptom. These features can also serve as feedback signals to a closed loop system. These features may include the overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta). In some cases, these features may be patient-specific or specific to a particular symptom, or both. For example, some of the features may be features found in a plurality of patients having the symptom; some of the features may be features in a particular patient which may not be found in a significant number of other patients having the symptom. In some embodiments, a combination of patient-specific features and symptom-specific features may be monitored to assess efficacy of treatment.

[0203] In a particular aspect, the closed loop system and methods provided herein may involve a recording of electrical signals from one or more regions of a patient’s brain, wherein the patient has a residual symptom associated with a neurological disorder or a psychiatric disorder during or after receiving treatment with medication, stimulation (e.g., electrical stimulation, magnetic stimulation, or ultrasound stimulation), or a combination thereof. The patient may then be further treated by application of stimulation, and electrical signals may be recorded from a region of the brain and compared to a pre-treatment recording. Features in the recorded signals that change after the stimulation would correspond to biomarkers that indicate whether the treatment is having an effect. The change in recorded signals can also optionally be correlated to the level of a residual symptom reported by the patient after the treatment. The change can be used for modulating the treatment in a closed loop system. For example, when the change in the recorded signal correlates with absence of the residual symptom, those features would indicate to a computing means of a closed loop system that further treatment need not be performed.

[0204] In some embodiments, one or more pattern recognition methods can be used in analyzing recorded brain electrical activity data to automate detection of brain activity features that are associated with a symptom of a neurological disorder or psychiatric disorder. The models and / or algorithms can be provided in machine readable format and may be used to correlate the levels of overall power, or power in specific frequency ranges (e.g., alpha, beta, gamma, delta, and / or theta) with a symptom to be treated with stimulation. Alternatively or additionally, coherence within certainspectral frequency bands or other features of network connectivity may be correlated with a residual symptom to be treated with electrical stimulation.Utility

[0205] The disclosed software, methods, and systems are useful for training patients to selfmodulate a neural signal that can be used to volitionally control their neurostimulation. A brain computer interface connected to a neural recording electrode and a data receiving device coupled to a display is used to provide useful neurofeedback to a subject, wherein the magnitude of a neural signal is represented as the height of a graphical object on the display. Using this visual neurofeedback, the subject increases or decreases the amplitude of the stimulation delivered to the subject by adjusting the vertical positioning of the graphical object on the display to reach a selected target. The BCI-based training will be useful for symptom control as well as promoting neural recovery from neurological and psychiatric disorders.

[0206] In an exemplified embodiment, patients with Parkinson’s disease were trained to modulate their intracranial cortical beta signal using at home neurofeedback provided through a brain computer interface. The cortical beta signal, represented as the height of a plane on a display, was used as an input biomarker for closed-loop neurostimulation. By modulating the brain signal to reach a certain threshold, patients voluntarily increased or decreased the amplitude of their neurostimulation in the absence of movement. This volitional neurostimulation technique should be applicable across a range of different neurological and psychiatric conditions to support personalized control of neurostimulation.

[0207] In addition to being useful for symptom control, the BCI-based training has applications in promoting neural recovery from conditions such as stroke, traumatic brain injury, or other brain damage. For example, the BCI can be used to train patients to turn on stimulation that is neurally reinforcing to promote neural plasticity enhancement.Examples of Non-Limiting Aspects of the Disclosure

[0208] Aspects, including embodiments, of the present subject matter described above may be beneficial alone or in combination with one or more other aspects or embodiments. Without limiting the foregoing description, certain non-limiting aspects of the disclosure numbered 1-189 are provided below. As will be apparent to those of skill in the art upon reading this disclosure, each of the individually numbered aspects may be used or combined with any of the preceding or following individually numbered aspects. This is intended to provide support for all such combinations of aspects and is not limited to combinations of aspects explicitly provided below.1. A method for treating a neurological disorder or psychiatric disorder in a subject, the method comprising:positioning a neural recording electrode at a first location in or near a brain region of the subject to record neural signal data;positioning a stimulator to deliver stimulation to a second location in the subject; connecting a brain computer interface (BCI) to the neural recording electrode and a data receiving device, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to the data receiving device, wherein the data receiving device comprises a processor coupled to a display;recording the neural signal data using the neural recording electrode while displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data; anddelivering stimulation to the second location in the subject using the stimulator, wherein the subject voluntarily increases or decreases the amplitude of the stimulation delivered to the second location in the subject by adjusting the vertical positioning of the graphical object on the display.2. The method of aspect 1 , wherein the graphical object resembles an airplane.3. The method of aspect 1 or 2, wherein the display further displays a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background.4. The method of aspect 3, wherein horizontal speed of the graphical object across the background is constant.5. The method of any one of aspects 1-4, further comprising training the subject to adjust amplitude of the stimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object on the display, wherein the subject adjusts the vertical positioning of the graphical object to reach a selected threshold.6. The method of any one of aspects 1-5, wherein the display further displays a target having a target shape, wherein said training comprises having the subject adjust amplitude of thestimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object such that the graphical object hits the target on the display.7. The method of aspect 6, wherein the target is positioned at a position in a range from 25% to 75% of the height of the display.8. The method of aspect 6 or 7, wherein the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.9. The method of any one of aspects 1-8, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.10. The method of any one of aspects 1-8, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.11. The method of any one of aspects 5-10, further comprising:disconnecting the BCI from the neural recording electrode and the data receiving device after said training; anddelivering stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.12. The method of any one of aspects 1-11, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.13. The method of any one of aspects 1-12, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.14. The method of any one of aspects 1-13, further comprising positioning a stimulation electrode at the second location, wherein the stimulation electrode is connected to the stimulator.15. The method of aspect 14, wherein the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.16. The method of aspect 14 or 15, wherein the stimulation electrode is positioned in a brain region, in a subgaleal space, on a head, in an epidural space, near a peripheral nerve, or near a muscle of the subject17. The method of any one of aspects 1-16, wherein the stimulation comprises electrical stimulation, magnetic stimulation, or ultrasound stimulation.18. The method of any one of aspects 1-17, wherein the stimulation comprises deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.19. The method of aspect 18, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.20. The method of aspect 17 or 18, wherein said delivering electrical stimulation comprises delivering electrical stimulation to a brain region, spinal cord, peripheral nerve, or muscle of the subject.21. The method of aspect 20, wherein the peripheral nerve is a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.22. The method of any one of aspects 1-21, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.23. The method of aspect 22, wherein the movement disorder is Parkinson’s disease or dystonia.24. The method of aspect 23, wherein the first location is a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and the second location is a subthalamic nucleus brain region or a globus pallidus internus brain region.25. The method of aspect 23 or 24, wherein the neural signal is a cortical beta signal.26. The method of aspect 25, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.27. The method of aspect 25 or 26, wherein the vertical position of the graphical object on the display is controlled by magnitude of cortical beta power.28. The method of aspect 27, wherein the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.29. The method of aspect 27 or 28, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.30. The method of aspect 27 or 28, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.31. The method of any one of aspects 27-30, wherein said training comprises downregulating or up-regulating the cortical beta power by adjusting the vertical position of the graphical object on the display.32. The method aspect 31, wherein said training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target.33. The method of any one of aspects 27-32, wherein the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold.34. The method of any one of aspects 27-32, wherein the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold.35. The method of any one of aspects 1-34, wherein the subject is required to remain immobile during the training.36. The method of aspect 35, further comprising:recording a video of the subject during the training; andchecking the video to determine if the subject remained immobile during the training.37. The method of any one of aspects 1-36, further comprising setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.38. The method of any one of aspects 1-37, wherein the psychiatric disorder is a mood disorder.39. The method of any one of aspects 1-38, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.40. The method of any one of aspects 1-39, wherein the stimulation is applied unilaterally or bilaterally.41. The method of any one of aspects 1-40, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.42. The method of aspect 41, wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neuraloscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.43. The method of any one of aspects 1-42, wherein the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model.44. The method of aspect 43, wherein the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output.45. The method of aspect 44, wherein the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal.46. The method of any one of aspects 43-45, wherein the machine learning model uses multivariate analysis of the neural signal data.47. The method of any one of aspects 44-46, wherein the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection.48. The method of any one of aspects 44-47, wherein the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination.49. The method of any one of aspects 44-48, wherein the machine learning model uses dimension decomposition to isolate the biomarker from a confound.50. The method of aspect 49, wherein the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof.51. The method of any one of aspects 43-50, wherein the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker.52. The method of any one of aspects 43-51, wherein the machine learning model is further used to distinguish movement artifacts from the neural signal.53. The method of any one of aspects 43-52, wherein the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data.54. The method of any one of aspects 43-53, wherein the machine learning model uses reinforcement learning threshold adaptation.55. The method of any one of aspects 43-54, wherein the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.56. The method of any one of aspects 1-55, wherein the data receiving device Is a computer, a cloud computing system, or a handheld device, optionally wherein the handheld device is a cell phone or tablet.57. The method of any one of aspects 1-56, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.58. The method of aspect 17, wherein the stimulator is an ultrasound transducer.59. The method of aspect 17, further comprising positioning a magnetic coil at the second location, wherein the magnetic coil is connected to the stimulator.60. The method of any one of aspects 1-59, further comprising automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject.61. The method of aspect 60, wherein the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.62. The method of any one of aspects 1-61, wherein said displaying the graphical object on the display comprises:displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; andsubsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time.63. The method of aspect 62, further comprising delivering stimulation using settings adjusted by the subject without displaying the graphical object on the display based on the training to achieve the learned neural state.64. The method of aspect 62 or 63, wherein the neural state is an induced neuroplasticity state or a patient-generated neural state.65. The method of aspect 64, wherein said delivering the stimulation induces the neuroplasticity state or the patient-generated neural state.66. The method of aspect 64 or 65, further comprising triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state.67. The method of aspect 66, wherein the assistive device is an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, or a language production device.68. The method of aspect 66 or 67, further comprising delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device.69. The method of any one of aspects 62-68, further comprising delivering transcranial stimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.70. The method of any one of aspects 1-69, wherein the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.71. The method of any one of aspects 1-70, wherein said delivering the stimulation promotes survival of motor neurons, strengthens weakened cortico-spinal connections, improves function or re-establishes function of a neural pathway, or any combination thereof.72. The method of any one of aspects 1-71, wherein the neural signal data comprises motor neuron data, and wherein said delivering the stimulation comprises delivering stimulation to the central nervous system, peripheral nervous system, or combination thereof.73. The method of aspect 72, wherein said delivering the stimulation comprises vagus nerve stimulation, functional electrical stimulation, deep brain stimulation, or any combination thereof.74. The method of any one of aspects 1-74, further comprising detecting cortico-spinal connectivity changes, motor evoked potential changes, connectivity or neuroplasticity changes, or any combination thereof resulting from said delivering the stimulation.75. The method of any one of aspects 71-74, wherein the subject is recovering from a stroke.76. The method of any one of aspects 1-75, wherein the method is performed while the subject is at home with remote monitoring of the subject by a clinician, optionally wherein the clinician interacts with the subject through telemedicine or telehealth.77. The method of aspect 76, further comprising cloud-based data transmission of the neural signal data to a data receiving device used by the clinician.78. The method of aspect 77, further comprising remote parameter adjustment by the clinician.79. The method of any one of aspects 1-78, further comprising cloud-based data transmission to the data receiving device used by the subject of a computer program for displaying the graphical object on the display for training the subject to voluntarily increase or decrease the amplitude of the stimulation delivered to the second location in the subject by adjusting the vertical positioning of the graphical object on the display.80. A computer-implemented method, the computer performing steps comprising: receiving recorded neural signal data from a neural recording electrode positioned at a first location in or near a brain region of the subject, wherein the neural recording electrode is connected to a brain computer interface (BCI) , wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to a data receiving device comprising a processor coupled to a display;displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data;adjusting settings of a stimulator based on the subject voluntarily increasing or decreasing the amplitude of the stimulation delivered to the subject by adjusting the vertical positioning of the graphical object on the display; andinstructing the stimulator to deliver the stimulation to the subject using the adjusted settings, wherein the stimulation is delivered to a second location in the subject.81. The computer-implemented method of aspect 80, wherein the graphical object resembles an airplane.82. The computer-implemented method of aspect 80 or 81 , further comprising displaying a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background.83. The computer-implemented method of aspect 82, wherein horizontal speed of the graphical object across the background is constant.84. The computer-implemented method of any one of aspects 80-83, further comprising displaying a target having a target shape to provide training to the subject, wherein said training comprises having the subject adjust amplitude of the stimulation by controlling the magnitude of theneural signal through adjusting the vertical positioning of the graphical object such that the graphical object hits the target on the display.85. The computer-implemented method of aspect 84, wherein the target is positioned at a position in a range from 25% to 75% of the height of the display.86. The computer-implemented method of aspect 84 or 85, wherein the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.87. The computer-implemented method of any one of aspects 80-86, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.88. The computer-implemented method of any one of aspects 80-87, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.89. The computer-implemented method of any one of aspects 80-88 further comprising instructing the stimulator to deliver the stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.90. The computer-implemented method of any one of aspects 80-89, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.91. The computer-implemented method of any one of aspects 80-90, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.92. The computer-implemented method of any one of aspects 80-91, wherein the stimulation comprises electrical stimulation, magnetic stimulation, or ultrasound stimulation.93. The computer-implemented method of any one of aspects 80-92, wherein the stimulation comprises deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.94. The computer-implemented method of aspect 93, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.95. The computer-implemented method of aspect 92 or 93, wherein the electrical stimulation is delivered to a brain region, spinal cord, peripheral nerve, or muscle of the subject.96. The computer-implemented method of aspect 95, wherein the peripheral nerve is a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.97. The computer-implemented method of any one of aspects 80-96, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.98. The computer-implemented method of aspect 97, wherein the movement disorder is Parkinson’s disease or dystonia.99. The computer-implemented method of aspect 98, wherein the first location is a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and the second location is a subthalamic nucleus brain region or a globus pallidus internus brain region.100. The computer-implemented method of aspect 98 or 99, wherein the neural signal is a cortical beta signal.101. The computer-implemented method of aspect 100, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.102. The computer-implemented method of aspect 100 or 101, wherein the vertical position of the graphical object on the display is controlled by magnitude of cortical beta power.103. The computer-implemented method of aspect 102, wherein the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.104. The computer-implemented method of aspect 102 or 103, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.105. The computer-implemented method of aspect 102 or 103, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.106. The computer-implemented method of any one of aspects 100-105, wherein said training comprises down-regulating or up-regulating the cortical beta power by adjusting the vertical position of the graphical object on the display.107. The computer-implemented method aspect 106, wherein said training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target.108. The computer-implemented method of any one of aspects 100-107, wherein the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold.109. The computer-implemented method of any one of aspects 100-107, wherein the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold.110. The computer-implemented method of any one of aspects 80-109, wherein the subject is required to remain immobile during the training.111. The computer-implemented method of aspect 110, further comprising:Instructing a video recording device to record a video of the subject during the training; and analyzing the video to determine if the subject remained immobile during the training.112. The computer-implemented method of any one of aspects 80-111 , further comprising setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.113. The computer-implemented method of any one of aspects 80-112, wherein the psychiatric disorder is a mood disorder.114. The computer-implemented method of any one of aspects 80-113, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.115. The computer-implemented method of any one of aspects 80-114, wherein the stimulation is applied unilaterally or bilaterally.116. The computer-implemented method of any one of aspects 80-115, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.117. The computer-implemented method of aspect 116, wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neural oscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.118. The computer-implemented method of any one of aspects 80-117, wherein the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model.119. The computer-implemented method of aspect 118, wherein the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output.120. The computer- implemented method of aspect 119, wherein the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal.121. The computer-implemented method of any one of aspects 118-120, wherein the machine learning model uses multivariate analysis of the neural signal data.122. The computer-implemented method of any one of aspects 118-121, wherein the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection.123. The computer-implemented method of any one of aspects 118-122, wherein the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination.124. The computer-implemented method of any one of aspects 118-123, wherein the machine learning model uses dimension decomposition to isolate the biomarker from a confound.125. The computer-implemented method of aspect 124, wherein the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof.126. The computer-implemented method of any one of aspects 118-125, wherein the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker.127. The computer-implemented method of any one of aspects 118-126, wherein the machine learning model is further used to distinguish movement artifacts from the neural signal.128. The computer-implemented method of any one of aspects 118-127, wherein the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data.129. The computer-implemented method of any one of aspects 118-128, wherein the machine learning model uses reinforcement learning threshold adaptation.130. The computer-implemented method of any one of aspects 118-129, wherein the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.131. The computer-implemented method of any one of aspects 80-130, wherein the data receiving device Is a computer or a handheld device.132. The computer-implemented method of aspect 131, wherein the handheld device is a cell phone or tablet.133. The computer-implemented method of any one of aspects 80-132, wherein the data receiving device is a cloud computing system.134. The computer-implemented method of any one of aspects 80-133, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.135. The computer-implemented method of any one of aspects 80-134, further comprising automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject.136. The computer-implemented method of aspect 135, wherein the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.137. The computer-implemented method of any one of aspects 80-136, wherein said displaying the graphical object on the display comprises:displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; andsubsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time.138. The computer-implemented method of aspect 137, further comprising instructing the stimulator to deliver the stimulation to the subject using settings adjusted by the subject without displaying computer-implemented the graphical object on the display based on the training to achieve the learned neural state.139. The computer-implemented method of aspect 137 or 138, wherein the neural state is an induced neuroplasticity state or a patient-generated neural state.140. The computer-implemented method of aspect 139, wherein said delivering the stimulation induces the neuroplasticity state or the patient-generated neural state.141. The computer-implemented method of aspect 139 or 140, further comprising triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state.142. The computer-implemented method of aspect 141 , wherein the assistive device is an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, or a language production device.143. The computer-implemented method of aspect 141 or 142, further comprising delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device.144. The computer-implemented method of any one of aspects 137-143, further comprising delivering transcranial stimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.145. The computer-implemented method of any one of aspects 80-144, wherein the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.146. The computer-implemented method of any one of aspects 80-145, wherein said delivering the stimulation promotes survival of motor neurons, strengthens weakened cortico-spinal connections, improves function or re-establishes function of a neural pathway, or any combination thereof.147. The computer-implemented method of any one of aspects 80-146, wherein the neural signal data comprises motor neuron data, and wherein said delivering the stimulation comprises delivering stimulation to the central nervous system, peripheral nervous system, or combination thereof.148. The computer-implemented method of aspect 147, wherein said delivering the stimulation comprises vagus nerve stimulation, functional electrical stimulation, deep brain stimulation, or any combination thereof.149. The computer-implemented method of aspect 147 or 148, wherein the subject is recovering from a stroke.150. The computer-implemented method of any one of aspects 80-149, further comprising detecting cortico-spinal connectivity changes, motor evoked potential changes, connectivity or neuroplasticity changes, or any combination thereof resulting from said delivering the stimulation.151. The computer-implemented method of any one of aspects 80-150, wherein the method is performed while the subject is at home with remote monitoring of the subject by a clinician, optionally wherein the clinician interacts with the subject through telemedicine or telehealth.152. The computer-implemented method of aspect 151, further comprising cloud-based data transmission of the neural signal data to a data receiving device used by the clinician.153. The computer-implemented method of aspect 152, further comprising remote parameter adjustment by the clinician.154. The computer-implemented method of any one of aspects 80-153, further comprising cloud-based data transmission of a computer program for performing the computer-implemented method to the data receiving device used by the subject.155. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the method of any one of aspects 80-154.156. A kit comprising the non-transitory computer-readable medium of aspect 155 and instructions for using the neurofeedback provided through the BCI to volitionally control stimulation for treatment of a neurological disorder or psychiatric disorder.157. A system for treating a neurological disorder or psychiatric disorder in a subject, the system comprising:a neural recording electrode;a stimulator;a data receiving device comprising a processor programmed according to the computer implemented method of any one of aspects 80-154;a display, wherein the display is connected to the data receiving device; anda brain computer interface (BCI), wherein the BCI is connected to the neural recording electrode and the data receiving device.158. The system of aspect 157, wherein the stimulator provides electrical stimulation, magnetic stimulation, or ultrasound stimulation.159. The system of aspect 158, wherein the electrical stimulation is applied unilaterally or bilaterally.160. The system of any one of aspects 157-159, wherein the stimulator provides deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.161. The system of aspect 160, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.162. The system of aspect 160, wherein the stimulator provides peripheral nerve stimulation or peripheral nerve field stimulation to a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.163. The system of any one of aspects 157-162, further comprising a video recording device.164. The system of any one of aspects 157-163, further comprising an external wearable monitor that can acquire accelerometry data, gyroscope data, magnetometer surface electromyographic (sEMG) data, or any combination thereof.165. The system of any one of aspects 157-164, wherein the data receiving device Is a computer or a handheld device.166. The system of aspect 165, wherein the handheld device is a cell phone or tablet.167. The system of any one of aspects 157-165, wherein the data receiving device is a cloud computing system.168. The system of any one of aspects 157-167, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.169. The system of any one of aspects 157-168, further comprising a stimulation electrode, wherein the stimulation electrode is connected to the stimulator.170. The system of aspect 169, wherein the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.171. The system of any one of aspects 157-170, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.172. The system of aspect 171, wherein the movement disorder is Parkinson’s disease or dystonia.173. The system of aspect 172, wherein the neural recording electrode is adapted for positioning in or near a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and wherein the stimulation electrode is adapted for positioning in a subthalamic nucleus region or a globus pallidus internus region.174. The system of any one of aspects 157-173, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.175. The system any one of aspects 157-173, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.176. The system of any one of aspects 157-175, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.177. The system of aspect 176, wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neural oscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.178. The system of aspect 177, wherein the neural signal is a cortical beta signal.179. The system of aspect 178, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.180. The system of aspect 178 or 179, wherein the vertical position of the graphical object on the display is controlled by cortical beta power.181. The system of aspect 180, wherein the vertical position of the graphical object on the display is controlled by the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.182. The system of aspect 180 or 181, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.183. The system of aspect 180 or 181, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.184. The system any one of aspects 157-170, wherein the psychiatric disorder is a mood disorder.185. The system any one of aspects 157-184, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.186. The system of any one of aspects 157-185, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.187. The system of any one of aspects 157-186, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.188. The system of aspect 157, wherein the stimulator is an ultrasound transducer.189. The system of aspect 157, further comprising a magnetic coil, wherein the magnetic coil is connected to the stimulator.

[0209] It will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.EXPERIMENTAL

[0210] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.

[0211] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.

[0212] The present invention has been described in terms of particular embodiments found or proposed by the present inventor to comprise preferred modes for the practice of the invention. It will be appreciated by those of skill in the art that, in light of the present disclosure, numerous modifications and changes can be made in the particular embodiments exemplified without departingfrom the intended scope of the invention. All such modifications are intended to be included within the scope of the appended claims.Example 1Volitional Deep Brain Stimulation following Brain Computer Interface Neurofeedback Training for Parkinson’s DiseaseIntroduction

[0213] Neurological and psychiatric disorders, ranging from Parkinson’s disease (PD) to depression, represent a global health burden with limited treatment efficacy for many patients. In this context, two rapidly advancing neurotechnologies in biomedical engineering, neuromodulation and braincomputer interface (BCI), have emerged as transformative tools for ameliorating pathological brain states. Integrating neuromodulation with BCI technologies in therapies would combine advantages from both technologies and create new intervention opportunities for neurological and psychiatric disorders (Herron et al., 2024). However, despite the conceptual synergy, there is a lack of research attempts and clinical validation for the seamless integration of these technologies in therapeutic contexts.

[0214] Deep Brain Stimulation (DBS) is an established and effective neuromodulation therapy for advanced neurological conditions, especially PD. However, conventional DBS (eDBS) operates with fixed stimulation parameters, which do not adapt to fluctuations in patient symptoms, medication status, or behavioral states (Little et al., 2013). This fixed property of eDBS limits its therapeutic precision and can result in suboptimal control and undesirable side effects. In practice, patients are usually provided with an externalized controller with which they can manually adjust stimulation within safe clinical limits. However, these manual controllers are difficult to operate while experiencing movement symptoms (e.g., tremor, bradykinesia) and almost impossible to implement on a moment-to-moment basis (Kaiser et al., 2012). To address these limitations, adaptive DBS (aDBS) therapies have emerged, which adjust stimulation in a dynamic way, usually in response to real-time neural biomarkers. Clinical studies have shown that aDBS can outperform eDBS in both efficacy and efficiency, and hence, is a promising approach to precise intervention for PD (Dixon et al., 2024; Gilron et al., 2021; Oehrn et al., 2024). Nonetheless, the complexity of aDBS programming increases substantially for complex symptom states, such as in patients with fluctuating motor symptoms or heterogeneous non-motor symptoms. These may require more sophisticated, time-intensive programming strategies to optimize therapy and avoid side effects (Hell et al., 2019; Waqle Shukla et al., 2017).

[0215] The success of aDBS hinges on identifying reliable biomarkers that accurately and reliably reflect relevant clinical states in a chronic term. In PD, one of the most established biomarkers is beta (13-30 Hz) oscillatory activity in the motor cortex-basal ganglia circuit, which correlates with motor symptom severity (Hammond et al., 2007; Little & Brown, 2014). Thus, aDBS therapies based on basal ganglia beta have been developed for PD and shown to outperform eDBS in both efficacy and efficiency (Little et al., 2013; Tinkhauser et al., 2017). However, this approach faces a number of challenges. The first challenge is that this signal can be confounded by factors such as physical movement, sleep, and the patient’s medication state. This challenge might be even larger for psychiatric conditions receiving neurostimulation therapies (Alagapan et al., 2023; Scangos et al., 2021) - such as depression, anxiety, and obsessive-compulsive disorder - where symptoms are more difficult to quantify objectively and there is a lack of clear physiomarkers (Ricciardi et al., 2023). A parallel but undiscussed challenge is that under any aDBS therapy over time, including those already in operation, patients may learn to regulate the target biomarker (e.g., beta signal), either implicitly or explicitly. This possibility is grounded in the principles of neurofeedback, where individuals can learn to alter a changing neural signal when the change is linked to a reward (Ros et al., 2020), such as the symptom alleviation due to neurostimulation change in the case of aDBS. This neurofeedback- mediated learning phenomenon could further complicate biomarker interpretation in PD. One promising way to address these challenges is to move beyond purely reactive stimulation and explore proactive stimulation approaches that incorporate a BCI system to enable patients’ selfregulation of neural signals.

[0216] At its core, the brain is fundamentally a self-regulating system, maintaining homeostasis across cognitive, emotional, and motor domains through continuous feedback and adjustment. In neurological and psychiatric disorders, this capacity for self-regulation becomes disrupted. From this perspective, current aDBS strategies can be conceived as a relatively crude attempt to restore system regulation — using a simple, externally-derived algorithm to compensate for dysfunctional internal control. In contrast, a proactive approach that explicitly incorporates the patient into this regulatory process has the potential to extend therapeutic efficacy substantially. By engaging the patient as an active participant, the entire brain — with its privileged access to internal states, intentions, and context — becomes part of the control system, rather than relying solely on external algorithms operating on limited simple biomarker information. This approach may be particularly relevant in Parkinson's disease, which can be conceptualized as a disconnection syndrome: patients often know precisely what movement they wish to execute, have sufficient muscle power, but cannotinitiate or execute that movement satisfactorily. A volitional neurostimulation system that allows patients to bridge this gap between intention and action through trained self-regulation may more directly address the core phenomenology of the disease than purely reactive stimulation approaches.

[0217] BCIs have been used for rehabilitative purposes to restore mobility in conditions such as stroke and spinal cord injury (Chaudhary et al., 2016; Daly & Wolpaw, 2008). During motor imagery training, brain signals from the motor cortex are decoded to provide an external feedback to the patient, in the form of robotic hand movement, electrical stimulation on the targeted skeletal muscle, or visual rewards on a computer screen (Cha et al., 2025; Choi et al., 2020; Do et al., 2011; Kim et al., 2025; Sharififar et al., 2018). For brain disorders stemming from central nervous system pathology (e.g., PD), pairing a BCI with DBS may represent a significant advance over traditional rehabilitative BCIs because feedback is delivered as intracranial electrical stimulation directly to neural circuits rather than indirectly to peripheral muscles or via the visual system. Direct intracranial stimulation in a state-dependent manner may engage reinforcement-like mechanisms and facilitate neural plasticity, providing a foundation for an augmented rehabilitation paradigm (Cavallo et al., 2025; Yttri & Dudman, 2016). We envisage that patients could theoretically learn to self-modulate neurostimulation therapy by entering desirable neural states. With repeated state-dependent stimulation over time, these neural states could be further promoted through selective induction of plasticity and reinforcement.

[0218] Here, we implemented a proof-of-principle demonstration of volitional DBS (vDBS) in two patients with PD, by integrating DBS with a BCI in a closed-loop system. With this vDBS system, patients could voluntarily adjust their DBS amplitude on a moment-to-moment basis. We first validated previous reports of self-regulation of beta activity via neurofeedback (Bichsel et al., 2021; Fukuma et al., 2018; He et al., 2020; Rohr-Fukuma et al., 2024), and then we showed that through this neurofeedback-guided self-regulation, patients could directly modulate their stimulation amplitude. The clinical aim was to test whether self-modulated neurostimulation is achievable, has side effects or can impact motor function, and to establish a foundation for volition-based DBS systems. It also explicitly tests the possibility of patients learning to regulate the subcortical beta signal over time, a target biomarker which has already been used in aDBS therapies for PD.Results

[0219] We designed an intracranial BCI game of airplane flying simulation to provide real-time visual neurofeedback of participants’ cortical beta power and guide them to regulate this brain signal via successful scoring in the game (FIG. 1A). To evaluate the feasibility of volitional BCI-DBS, we implemented a two-stage BCI protocol comprising a neurofeedback training phase followed by avDBS test phase (FIG. 1B). Two right-handed PD patients with implanted sensing-enabled investigational Summit RC+S neurostimulators were recruited from a parent clinical trial (ClinicalTrials.gov registration: NCT03582891). They participated in this fully remote, home-based study (see Methods for protocol details). They completed all experiment sessions in an ON- medication state. During the training phase, participants played the BCI game to learn to down- regulate low beta power (15-20 Hz) in the left primary motor cortex through visual neurofeedback without making physical movements. Once this ability was established in the training phase, the same cortical beta signal was used in the test phase as a real-time control input to modulate stimulation amplitude dynamically (i.e., vDBS). We assessed patients’ game performance and associated neural activity, as well as pre- and post-game motor performance.BCI Neurofeedback game performance

[0220] Across the 7 training sessions, both participants showed performance improvements in the BCI neurofeedback game under constant DBS (FIG. 2A). During the testing session (FIG. 2B), we included additionally an ‘increase’ and a ‘decrease’ DBS policy (see Methods for details) to test game performance under vDBS. Both participants’ performance in the BCI game was not significantly different across the DBS policies (Patient 1: F(2, 33) = 0.38, p = .687; Patient 2: F(2, 33) = 0.02, p = .977), indicating that the different stimulation conditions did not impair learned neurofeedback control. Of note, Patient 1 performed their 4th training session approximately 3 hours earlier than their usual time of day, which potentially explains the drop in score in this particular session.Successful volitional regulation of beta power

[0221] During the testing sessions, both participants showed excellent capability of self-modulating their beta power in the ‘regulation’ trials. Under constant therapeutic DBS, the trial-level power reduction in ‘regulation’ trials was observed across a broad beta band (13-30 Hz) in cortical channels (FIG. 3A; Patient 1: f(158) = -7.10, p < 1e-10; Patient 2: f(158) = -19.49, p < 1e-10), which occurred despite the BCI-neurofeedback target being confined to a narrower low beta band. Moreover, a smaller but significant attenuation in a high-beta power (20-30 Hz) in the subcortical subthalamic nucleus (STN) was also evident in both patients, when training neurofeedback on cortical signals, suggesting that learned cortical regulation propagated through the motor network (FIG. 3B; Patient 1: f(158) = -4.43, p = 1.78e-5; Patient 2: f(158) = -2.14, p = 0.034).

[0222] Demonstrating moment-to-moment regulation, distinct differences in cortical beta power distributions between the ‘regulation’ and ‘rest’ conditions were evident in online real-time data (i.e., before trial-level averaging) for both patients (FIG. 3C; Patient 1: t(56082) = -72.99, p < 1e-10; Patient2: f(55730) = -120.57, p < 1e-10). Based on the data from two most recent training sessions, we set a personalized optimal beta power threshold for each participant during testing sessions. With this threshold, the low vs. high beta state percentage was significantly different between the ‘regulation’ and ‘rest’ conditions, as shown by the condition x beta state interaction (FIG. 3D), Patient 1, F(1, 915) = 314.7, p < 1e-10, Patient 2, F(1, 916) = 1540.7, p < 1e-10. This interaction reveals that participants spent most of the time in a low beta state in ‘regulation’ condition (Patient 1: 75.0%, Patient 2: 86.1%), while they spent much less time in in a low beta state in ‘rest’ condition (Patient 1: 50.8%, Patient 2: 45.6%).Volitional control of adaptive DBS

[0223] During testing, patients' learned beta regulation enabled volitional control of stimulation amplitude under vDBS policies. In the ‘constant’ policy, the vDBS control algorithm directed the participants’ stimulation amplitude to stay constant at their regular clinical levels (Patient 1 = 3.0 mA, Patient 2 = 4.0 mA ) regardless of their cortical beta state. In the ‘increase’ policy, the vDBS control algorithm was designed to increase the participants' stimulation amplitude (Patient 1: 3.0 — > 3.4 mA, Patient 2: 3.7 — > 4.3 mA) when their cortical beta dropped to the personalized threshold. Conversely, in the ‘decrease’ policy, the control algorithm was designed to decrease stimulation amplitude (Patient 1: 3.0 — ► 2.6 mA, Patient 2: 4.3 -> 3.7 mA) when the cortical beta dropped to the threshold.

[0224] In order to test the effects of condition (rest vs. regulation) x DBS policy (constant, increase, decrease) together, we ran a two-way ANOVA on the target cortical beta power (15-20 Hz). A main effect of condition indicated that the beta power was significantly lower in the ‘regulation’ trial compared to the Test’ trials across DBS policies (FIG. 4A; Patient 1: F(1, 474) = 212.9, p < 1e-10; Patient 2: F(1, 474) = 737.7, p < 1e-10). In addition, we found a significant condition x DBS policy interaction on the beta power in Patient 1, F(2, 474) = 4.16, p = .016, supporting that the beta power regulation capability was stronger in ‘constant’ policy (Cohen’s d = 1.67) compared to ‘increase’ (Cohen’s d = 1.17) or ‘decrease’ policies (Cohen’s d = 1.18). This interaction was not significant in Patient 2, F(2, 474) = 1.58, p = .207. Furthermore, we examined the temporal dynamics of cortical beta in each trial and calculated the time taken from the trial onset for it to drop to the threshold. Consistently, a condition x DBS policy ANOVA revealed a main effect of condition that patients were much faster to reach the beta threshold in ‘regulation’ trials than in ‘rest’ trials (FIG. 4B; Patient 1: F(1, 473) = 61.9, p < 1e-10; Patient 2: F(1, 474) = 42.4, p = 1.9e-10). Additionally in Patient 2, there was a significant condition x DBS policy interaction, F(2, 474) = 5.58, p = .004, suggesting that this regulation vs. rest condition effect was stronger in ‘constant’ policy (Cohen’s d - 0.78) than ‘increase’ (Cohen’s d= 0.41) and ‘decrease’ policies (Cohen’s d= 0.53).

[0225] To quantify the volitional stimulation change, we calculated the trial-level average stimulation amplitude in each trial condition and for each DBS policy. We found a significant condition x DBS policy interactions, Patient 1, F(1, 476) = 78.4, p < 1e-10, Patient 2, F(1, 474) = 366.8, p < 1e-10 (FIG. 4D). As designed, both patients received significantly greater stimulation in the ‘regulation’ condition (when they had self-modulated their own beta signal) than the ‘rest’ condition under the ‘increase’ policy (Patient 1: mean amplitude change = 0.09 mA, f(159) = 6.88, p = 1.3e-10; Patient 2: mean amplitude change = 0.23 mA, f(158) = 14.62, p < 1e-10). Conversely, under the ‘decrease’ policy, they received significantly less stimulation in the ‘regulation’ condition than the ‘rest’ condition (Patient 1: mean amplitude change = -0.09 mA, f(159) = -7.69, p < 1e-10; Patient 2: mean amplitude change = -0.24 mA, f(158) = 16.80, p < 1e-10). This indicates that both of the patients voluntarily modulated their DBS stimulation by regulating their cortical beta power. That is, they successfully performed volitional DBS to modify their DBS amplitude setting through voluntary self-modulation in the absence of explicit movements (confirmed through inspection of video recordings).Effects on motor performance

[0226] We also assessed the motor performance of the dominant hand under each DBS policy before and after neurofeedback (NF) blocks with a key tapping task (see Methods). We hypothesized (1) an effect of DBS policy that the ‘increase’ policy would improve and the ‘decrease’ policy would worsen motor performance relative to the ‘constant’ policy, and (2) an effect of NF that motor performance would be better post-NF than pre-NF. We tested them in a 3 (DBS policy: constant, increase, decrease) x2 (neurofeedback: pre-NF and post-NF) ANOVA.

[0227] We found evidence for DBS effect in Patient 1 and evidence for NF effect in both patients. In Patient 1 under the ‘decrease’ policy, we observed that the tapping speed was slower (F(2,157) = 5.51, p = .005, FIG. 5A left) and there were more errors (F(2,157) = 3.64, p = .029, FIG. 5B left) than the ‘constant’ and ‘increase’ policies, supporting the presence of possible bradykinesia due to understimulation under ‘decrease’ policy (i.e. smaller stimulation amplitude). Motor performance under the ‘increase’ policy was not superior to the ‘constant’ policy (tapping speed: f(118) = 0.97, p = 0.336; error rate: t(118) = 0.33, p = 0.745), which could be explained by possible dyskinesia due to the combined effects of higher stimulation and movement-related beta desynchronization. In Patient 2, we found a main effect of NF on the tapping speed showing that the patient was faster after NF blocks than before (FIG. 5A right), F(1,324) = 8.93, p = .003, supporting a possible direct effect of the NF-guided down-regulation of cortical and associated subcortical beta activities. Patient 1 had a similar improvement of tapping speed across NF blocks under the ‘constant’ policy (FIG. 5A left), t(78) = 2.24, p = 0.028. Of note, the two patients had different approaches to setting stimulationamplitudes for their ‘increase’ and ‘decrease’ vDBS policies, which may explain why only Patient 1 experienced a DBS effect. In Patient 1 , we set the lower amplitude for the ‘increase’ policy equal to the upper amplitude for the ‘decrease’ policy. In contrast, Patient 2 had identical amplitude ranges for both policies but reversed the order, leading to a larger average amplitude difference in Patient 1 (FIG. 4D). These two approaches of volitional ‘increase’ and ‘decrease’ policies could be used to fulfill different stimulation needs.Discussion

[0228] In two patients with Parkinson’s disease, we show a demonstration of volitional DBS in a naturalistic home setting. Through BCI neurofeedback training, the patients were able to voluntarily modulate their cortical (and associated subcortical) beta power which was used as an input in a closed-loop DBS system for adjusting stimulation amplitude. This proof-of-principle establishes volitional DBS as a feasible neurostimulation paradigm with two distinct implications: as a novel therapeutic approach and as an underappreciated factor in existing adaptive neuromodulation systems.Therapeutic potential of volitional neurostimulation

[0229] The volitional approach addresses a limitation of current aDBS: reliance on externally-derived algorithms with access to only a fraction of the neural information available to the patient's own brain. Here, by recruiting the patient as an active participant, the entire brain — with privileged access to intentions, context, and internal states — becomes part of the therapeutic control system. This may be particularly valuable for conditions where reliable biomarkers remain elusive, including psychiatric disorders, complex motor phenotypes, and heterogeneous symptom presentations. In this approach, the patient is recruited as an active contributor to enhance therapeutic control, by leveraging the brain’s unique abilities to access intentions and to self-regulate internal states. This strategy may hold particular promise for conditions in which reliable biomarkers are lacking, such as psychiatric disorders, complex motor phenotypes, and conditions with heterogeneous symptom presentations.

[0230] We used a classical neural signal of movement, beta power at M1 cortex, as the target biomarker in BCI neurofeedback for proof of principle. One strength of beta power is that it is relatively easy to modulate with neurofeedback training, as demonstrated by prior research (Bichsel et al., 2021; Fukuma et al., 2018; He et al., 2020). A further advantage is that down-regulation of cortical beta itself was associated with an improvement in motor performance (FIG. 5A), providing an additive therapeutic effect. However, this cortical beta signal could be confounded by physical movements in real life. When making a physical movement, cortical beta oscillations are likely todesynchronize (i.e., a decrease in power). This property makes our vDBS under ‘increase’ policy analogous to the recently developed movement-responsive DBS (Dixon et al., 2024), and both approaches demonstrated better motor performance than the inverted policy (i.e. ‘decrease’ policy here). Along with the cortical beta target biomarker, there was an associated reduction in subcortical beta during regulation trials in both patients (FIG. 3B). In Parkinson’s disease, beta activities (particularly subcortically) are related to the patient’s dopaminergic medication levels and motor symptoms (Giannicola et al., 2010; Jenkinson & Brown, 2011) so that its baseline level may fluctuate along with the medication cycle and beta bursts are more likely to occur when off-medication. Whether other neural signals could be a robustly modulable biomarker with fewer confounds warrants further research.

[0231] Besides movement disorders, the volitional neuromodulation methodology presents a potentially powerful extension of current rehabilitative BCIs. A cornerstone of effective motor rehabilitation, particularly post-stroke, is the induction of meaningful neuroplasticity within the lesioned motor network. Current rehabilitative BCIs typically rely on peripheral feedback — such as functional electrical stimulation, robotic actuation, or visual rewards — to reinforce the patient’s motor imagery (Chaudharyet al., 2016; Daly&Wolpaw, 2008). In contrast, our system couples BCI-guided motor imagery with direct electrical neuromodulation applied centrally to the motor circuit. This direct intervention offers a fundamentally more precise, and potentially more potent mechanism for immediately reinforcing the activity of surviving motor neurons and strengthening weakened corticospinal connections. Furthermore, the dual feedback mechanism, which provides both the visual BCI training signal and the therapeutic effect of contingent DBS, could be leveraged to establish or reestablish functional neural pathways with greater efficacy than peripheral stimulation alone. Therefore, the volitional BCI-DBS paradigm developed here provides a potentially powerful pathway for accelerating motor recovery and enhancing functional gains after severe brain injury. An important next step for this approach to stroke rehabilitation would be to assess cortico-spinal connectivity pre- / post-NF training or over extended vDBS use.Implications for adaptive DBS therapies

[0232] Despite the opportunities created by volitional neurostimulation, our findings also suggest a potential caveat for existing aDBS therapies and those that are being developed, and notably for beta-based aDBS policies to treat PD. We showed that the regulating capability of cortical and subcortical beta power is learnable, and this is potentially true for other brain signals and targets (Bichsel et al., 2021; Fukuma et al., 2018). Theoretically, if patients under a beta-based adaptive DBS policy either like or dislike the effects of their stimulation, they could implicitly learn to modulatetheir stimulation amplitudes by controlling their own brain signals. Observation and interview of our two patients revealed that they shifted their beta-regulating strategy from explicit to more implicit during our BCI-neurofeedback protocol, as observed in other BCI training paradigms in general, which underlines the possibility of implicit learning. When this implicit learning is strong enough, patients receiving beta-based aDBS may even learn to modulate stimulation at motor preparation stage due to the predicted movement need. Acknowledging this potential caveat in beta-based aDBS therapies, scientists and clinicians need to monitor the possibility of self-modulating beta signals over time and design systems to prevent patients from harnessing it incorrectly. Alternatively, it may be advantageous for aDBS therapies to transition to biomarkers that are less susceptible to implicit selfmodulation, such as gamma signals in the context of PD.Practical considerations

[0233] To transform the concept of volitional neurostimulation into a viable therapy for neurological and psychiatric patients, it is crucial to explore its practicality and appeal in clinical scenarios. A primary challenge lies in assessing whether requiring patients to control their own stimulation through mental effort would be engaging and effortless, or conversely, distracting and effortful. Our neural system learns to regulate many systems implicitly and autonomously (walking, talking, thinking), therefore it is possible that as patients move from explicit to implicit control, the cognitive load / effort of volitional neuromodulation would reduce and may be even eliminated. A second challenge concerns the identification of a target neural signal or a combination of neural signals without interference from daily activities. For instance, beta signals are responsive to movements (Pfurtscheller et al., 2003) and low-frequency signals (delta and theta) are responsive to sleep (Schwartz & Roth, 2008), making them potentially suboptimal targets. Using an orthogonal signal to movement or sleep for volitional neuromodulation might require longer and more sophisticated training, but may be more beneficial in the long term. Research has shown that patients are able to control a BCI cursor linked to cortical gamma signals while making natural movements simultaneously (Bashford et al., 2018). A third challenge concerns in real-world settings whether patients can identify optimal time points to self-regulate their neurostimulation. In the current study, patients were explicitly cued to regulate their neural activity and thereby modulate their neurostimulator through the BCI game design. Applying volitional neurostimulation at a suboptimal timing may lead to unexpected results: For instance, a PD patient experiencing increased anxiety might think they need stronger stimulation to regulate the symptom while their subcortical beta is in fact not high, which might result in “self-inflicted dyskinesia." Therefore, training patients to identify when to self-regulate is an important future step. A fourth challenge will depend on how well patientscan sustain volitional control of neurofeedback-trained brain signals in naturalistic settings. We anecdotally observed that our two patients were still able to perform cortical beta-regulation after a one-month gap but future studies are required to test the long-term practicality of volitional neurostimulation.

[0234] To satisfy both physiological and volitional needs, a possible hybrid approach is to design a closed-loop neurostimulation system with a weighted combination of reactive aDBS linked to one biomarker (Oehrn et al., 2024) plus a secondary volitional self-modulation mechanism linked to an independent biomarker. These could operate on different time scales with the reactive aDBS on a timescale of minutes to hours and the vDBS on one of seconds. Certain disorders may inherently lend themselves to this method more effectively than others, potentially due to the neurophysiology of the symptoms or the patient’s capacity for cognitive engagement. Careful consideration and rigorous testing are necessary to identify which patient populations might benefit most from volitional neurostimulation, ensuring that it is both a practical and beneficial therapeutic option.Conclusion

[0235] We establish that patients can learn to volitionally control intracranial neurostimulation through BCI neurofeedback-trained self-regulation of neural activity (i.e. volitional DBS). This volitional DBS paradigm offers a new approach for personalized, patient-driven neuromodulation and highlights the importance of considering learned biomarker regulation in adaptive neurostimulation therapies. This vDBS approach could be applied to a range of different neuropsychiatric conditions and brain stroke or injury rehabilitation to support personalized control of neurostimulation and augmented rehabilitation.MethodsParticipants

[0236] Between July to December 2024, we initially recruited three PD patients based on their willingness to participate from a parent clinical trial of chronic multi-site brain recording (ClinicalTrials.gov registration: NCT03582891). One participant consistently felt drowsy and could not stay fully awake during the training sessions and was discontinued from participation, leaving a final sample of two patients (Table 1). They were implanted with the Medtronic Summit RC+S investigational neuromodulation system with DBS electrodes implanted at the subthalamic nucleus (STN) and an electrocorticography (ECoG) strip placed over the sensorimotor cortex. The participants were both right-handed, and received constant bilateral DBS as a clinical treatment(Patient 1 left DBS: stimulation amplitude = 3.0 mA, frequency = 130.2 Hz, pulse width = 60 ps, contact=1-C+; Patient 2 left DBS: stimulation amplitude = 4.0 mA, frequency = 150.6 Hz, pulse width = 60 ps, contact=1-C+). Informed written consent was provided by the patients, and the protocol was approved by the UCSF Institutional Review Board.Table 1. Participants demographics, clinical characteristics and stimulation settingsParticipant ID Patient 1 Patient 2Participant CharacteristicsAge (years) 68 64Gender Male MaleHandedness Right RightPD Diagnosis Year 2000 2015DBS Implant and Stimulation ParametersImplant Date 7 / 16 / 2019 5 / 19 / 2021Stimulation Target STN STNLaterality Bilateral BilateralStimulation Contact L STN: C+, 1- L STN: C+, 1- R STN: C+, 1-, 3- R STN: C+, 1-, 2- eDBS Amplitude (mA) L STN: 3.0 L STN: 4.0R STN: 4.2 R STN: 4.4Pulse Width (us) L STN: 60 L STN: 60R STN: 60 R STN: 70Stimulation Frequency (Hz) L STN: 130 L STN: 150.6R STN: 130 R STN: 150.6Symptoms and Clinical CharacteristicsMedication Details Carbidopa-Levodopa (Sinemet) Carbidopa-Levodopa (Rytary)50-200 mg CR (3 times daily) 23.75-95 mg ER (2 capsules 3 times daily)LEDD (mg) 450 342UPDRS-III (OFF med, pre-DBS) 45 35UPDRS-III (ON med, pre-DBS) 23 12UPDRS-III (Pre-NF) 24 15UPDRS-III (Post-NF) 23 19MoCA 30 / 30 27 / 30PD - Parkinson’s disease, R- right, L- left, eDBS - constant Deep Brain Stimulation, UPDRS - Unified Parkinson’s Disease Rating Scale, MoCA - Montreal Cognitive Assessment, None of the participants suffered from Dementia.Brain-computer interface

[0237] We implemented a brain-computer interface (BCI) training game which used an airplane as a cursor which patients had to drive towards targets (FIG. 1), controlled by real-time intracranial brain signals. In the game, which was developed in the Unity (unity.com / ) video game engine using a combination of purchased art assets and custom software development, the plane flies horizontally at a constant speed (maintaining a fixed horizontal position on the screen with the background moving from right to left). The vertical position of the plane was set to be controlled by a Summit RC+S embedded computation of the beta power of the patient's left primary motor cortex (contralateral to their dominant hand) streamed to the task computer (Microsoft Surface) using the Summit API. In Patient 1, the vertical position of the plane was set to be proportional to the log- transformed beta power. In Patient 2, the vertical position of the plane was set to be inversely proportional to the log-transformed beta power to counterbalance the setup. Through pilot data, we calibrated the scaled beta power to be close-to-normally distributed on a scale of 0-100, mapping to the plane position on the screen (0 = bottom of screen, 100 = top of screen).Procedure

[0238] Participants went through a protocol of 11 total sessions consisting of 7 training sessions and 4 testing sessions, conducted fully remotely in their homes on different days. On each day, participants started the session one hour after ingestion of the standard PD Levodopa medication. Researchers interacted with the participants remotely through video telemetry, operated the task computer via remote desktop, and updated stimulation programming settings remotely. On Days 1- 7 (training sessions), they were trained on down-regulating their cortical beta activity by playing the plane simulation BCI game with neurofeedback at constant DBS (normal therapeutic level). At the beginning and end of the training period, a neurologist performed a motor examination of the participants through video telemetry using MDS-UPDRS-III and the video recordings were scored by another neurologist blinded to experiment conditions (FIG. 6). On Days 8-11 (testing sessions), participants played the BCI airplane simulation game for 1) two blocks during constant DBS policy,2) two blocks with ‘increase’ adaptive DBS policy, and 3) two blocks with a ‘decrease’ adaptive DBS policy, during each session. They were instructed not to make physical movements or tense up muscles during the game and videotaped. All sessions were started one hour after patients took their regular clinical dopaminergic medication dose.

[0239] During training sessions, participants played the BCI airplane simulation game using selfmodulation of their cortical beta power. There are two conditions of trials during the game: ‘rest’ trials and ‘regulation’ trials. During the rest trials, participants were instructed to relax and observe the natural fluctuation of the plane (related to natural cortical beta fluctuations) but not attempt to modulate the plane’s position. During the regulation trials, there were spherical targets that appeared at either the 25th or 75th percentile of the screen height (Patient 1 — > 25%, Patient 2 75%). Compared to the rest condition, participants needed to down-regulate their cortical beta power in order to hit the targets to score. The participants’ goal was to hit as many targets as possible, as indicated by a cumulative score at the top right of the screen. In each game block, there were interleaved 10 rest trials + 10 regulation trials, with the order counterbalanced across blocks (i.e., rest-regulation or regulation-rest). Participants completed 3 blocks of the game (20 trials per block, 15s per trial, 60s break between blocks). The total session length was ~ 20 minutes. Before and after the neurofeedback game, participants completed a movement key tapping task, in which they tapped the left and right arrow keys alternately with their index and middle finger of the dominant hand respectively for 10 times as fast as they could, and repeated this procedure for 10 times. Rapid alternating movement speed was quantified as well as key errors.

[0240] During testing sessions, participants played the same BCI plane simulation game under three DBS policies: 1) an ‘increase’ policy, 2) a ‘decrease’ policy, and 3) a ‘constant’ (control) policy. Participants completed two blocks of the game under each policy. Under the ‘increase’ policy, the stimulation amplitude increases when the target brain signal (i.e., cortical beta power) falls below the pre-set threshold. Under the ‘decrease’ policy, the stimulation amplitude decreases when the target brain signal falls below the pre-set threshold. Under the ‘constant’ policy, the stimulation amplitude remains constant at the clinical level. The temporal order of the three DBS policies was counterbalanced across sessions and patients were blind to stimulation conditions. Under each DBS policy, participants also completed the same key tapping task at the beginning of the session and after the corresponding game blocks.Neural data streaming

[0241] During the neurofeedback BCI game, intracranial time-domain signals of the participants’ left hemisphere (two cortical channels, one STN channel) were recorded with a sampling rate of 250 Hz.The beta power (15-20 Hz) of the primary motor cortex (M1, contact 8-10) was calculated online in the embedded device by Fast Fourier Transform (FFT) and used for neurofeedback in the BCI airplane simulation game. The online time frequency deconstruction (i.e. FFT) was performed with a window size of 1024 points and an interval of 100 points. The resulting beta power was streamed to a gRPC-enabled microservice (Open Mind Neuromodulation Interface, github.com / openmind- consortium / OmniSummitMicroservice-PublicRelease) on the task PC (Roarr et al., 2021), which relayed the control signal to the BCI task in Unity where it was mapped to the airplane vertical position.Adaptive deep brain stimulation

[0242] We used the data from the last two training sessions to determine the beta power threshold for adaptive DBS. The threshold was determined to maximize the differentiation between rest vs. regulation trials during the neurofeedback game by maximizing the F1 score. F1 score is a harmonic mean of precision (TP / TP+FP) and recall (TP / TP+FN).

[0243] The control algorithm was set up so that when the target brain signal is below the pre-set threshold, it is defined as being in 'stateO’. When the target brain signal is above the pre-set threshold, it is defined as being in ‘stateT. Across all adaptive DBS policies, the overall upper limit of the stimulation amplitude was tested with the participants and set at a maximal level without introducing distraction or discomfort. The overall lower limit of stimulation was set symmetrical to the upper limit in reference to the participant’s clinical level. We tested two different sets of adaptive policies in the two participants. In Patient 1, the lower level of stimulation amplitude in the ‘increase’ policy and the upper level in the ‘decrease’ policy were set the same as the clinical stimulation level (i.e., constant policy: stim=3.0 mA; increase policy: stateO = 3.4 mA, statel = 3.0 mA; decrease policy: stateO = 2.6 mA, statel = 3.0 mA). In Patient 2, the lower and upper levels within the ‘increase’ and ‘decrease’ policies were set symmetrically in reference to the clinical level (i.e., constant policy: stim=4.0 mA; increase policy: stateO = 4.3 mA, statel = 3.7 mA; decrease policy: stateO = 3.7 mA, statel = 4.3 mA). The ramping rate of stimulation change was tested between the lower and upper stimulation levels and set to the maximum within patient tolerance (8.67 mA / s) without inducing side effects (e.g., paresthesias).Data analysis

[0244] Intracranial neural data were first preprocessed with Openmind ProcessRCS (github.com / openmind-consortium / Analysis-rcs-data) in Matlab R2022a. All other data analysis was performed in Python 3.11.4. Power spectral density was computed with Welch's method(psd_array_welch in MNE time-frequency) with a Hamming window, 250-point segments (1s sample), and 50% overlap. ANOVA models were performed using statsmodels. Following ANOVA, two-sample t-tests (scipy) were performed as planned contrasts in the event of statistically significant ANOVA effects. Thus, no adjustments were made for multiple comparisons. Data were visualized with seaborn and matplotlib packages. All error bars / ribbons in plots represent 95% confidence intervals bootstrapped for 1000 times.References

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[0278] Yttri, E. A., & Dudman, J. T. (2016). Opponent and bidirectional control of movement velocity in the basal ganglia. Nature, 533(7603), 402-406.Example 2Volitional Stimulation ApplicationsStroke and NeurorehabilitationNeuroplasticity Induction Mechanisms

[0279] Rationale: State-dependent stimulation through "reinforcement-like mechanisms" facilitates neural plasticity. Neuroplasticity can be induced through temporally-contingent volitional neurostimulation with state-dependent stimulation delivery timed to patient-generated neural states. This approach can be used for reinforcement of surviving motor neuron activity through contingent stimulation and strengthening of weakened cortico-spinal connections via BCI-triggered stimulation.Dual Feedback Rehabilitation Systems

[0280] Rationale: Combining visual BCI feedback with therapeutic stimulation feedback creates a synergistic training paradigm. The rehabilitation system can provide simultaneous visual neurofeedback and therapeutic stimulation feedback, which can be used for re-establishingfunctional neural pathways using dual feedback mechanisms and optimizing rehabilitation protocols combining BCI motor imagery training with contingent central / peripheral stimulation.Multimodal Effector Systems for Rehabilitation

[0281] Rationale: The volitional BCI paradigm can drive multiple output modalities beyond DBS.For example, functional electrical stimulation (FES) can be controlled by volitional neural signal modulation. Powered exoskeleton / orthosis actuation can be triggered by BCI-trained neural state changes by a subject. Robotic rehabilitation devices may also be controlled via volitional biomarker modulation by a subject. Vagus nerve stimulation (VNS) may be paired with motor training for stroke recovery. Transcranial stimulation (TMS / tDCS / tACS) delivery can be made contingent on volitional neural states. Central stimulation (DBS / VNS / TMS) can be combined with peripheral effectors (FES / exoskeleton).Connectivity Monitoring & Outcome Assessment

[0282] Rationale: Objective outcome measurement can be used for adaptive protocol adjustment.Cortico-spinal connectivity changes can be assessed during or after volitional neurostimulation training. Motor evoked potential (MEP) changes can be monitored as rehabilitation biomarkers. Adaptive rehabilitation protocols may be modified based on connectivity / plasticity measurements.Machine Learning and Al EnhancementML-Based Personalized Biomarker Discovery

[0283] Rationale: Multivariate neural biomarkers encompassing multiple features (frequencies and channels) may be identified for personalized medicine. Machine learning algorithms may be used for identification of patient-specific optimal biomarker features (frequency, channel, phase). Multivariate neural signal analysis can be used to determine personalized control signals. Machine learning can be used for automated mapping of individual peak frequencies for biomarker selection and optimization of biomarker selection to maximize regulation-versus-rest discrimination.ML-Based Confound Rejection

[0284] Rationale: Neural signals may be confounded by movement, medication, or sleep. Machine learning algorithms can be used for rejection and separation of movement artifacts from volitional neural signals, dimension decomposition (PCA, ICA, etc.) to isolate a volitional biomarker from confounds, and context-aware biomarker interpretation accounting for medication state, sleep state,and / or activity level. Multimodal sensor fusion (accelerometry, EMG, medication logs) can be used for confound identification.ML-Based Adaptive Threshold Optimization

[0285] Rationale: F1 score maximization can be used for threshold setting. Machine learning can automate and continuously optimize threshold setting. For example, threshold optimization can be automated using classification metrics (F1, AUC-ROC, sensitivity / specificity). Threshold recalibration can be performed continuously or periodically based on accumulated training data. Reinforcement learning can be used for long-term threshold adaptation. Transfer learning can be used to reduce calibration time for new patients using population models.Cortico-Subcortical Signal Propagation

[0286] Rationale: Cortical beta downregulation propagates to the STN (Patient 1: p=1.78e-5;Patient 2: p=0.034), as shown in Example 1. This demonstrates network-level effects from cortical training. Cortical signal modulation produces measurable subcortical signal changes. Systems can be configured to detect and / or utilize cortico-subcortical coupling with training protocols leveraging cortical-to-subcortical propagation for therapeutic effect.Remote / Home-Based Implementation

[0287] Rationale: The entire study can be conducted remotely at home via telemedicine. This has major implications for scalability and reimbursement. Remote neurofeedback training systems can be integrated with telemedicine / telehealth. Home-based volitional neurostimulation can be combined with remote clinician monitoring and oversight, cloud-based data aggregation and analysis, and remote parameter adjustment with synchronous training session review and remote stimulation programming.Hybrid Reactive + Volitional Architecture

[0288] Rationale: Hybrid systems may use a weighted combination of reactive aDBS linked to one biomarker plus a secondary volitional self-modulation mechanism linked to an independent biomarker on different timescales. For example, a hybrid closed-loop system may combine autonomous biomarker-reactive stimulation with patient-volitional control with multi-timescale control architectures (e.g., reactive on minutes-hours, volitional on seconds). Systems may use independent biomarkers for reactive versus volitional control loops. Systems may use weighted or blended control algorithms combining algorithmic and patient-driven inputs.Safety, Training & Outcome RefinementsTraining Retention & Maintenance Protocols

[0289] Rationale: Patients maintained beta regulation after a one-month gap. Formalizing retention assessment and booster protocols adds value. Skill retention can be assessed over time without continuous training. Maintenance / booster training protocols can be triggered by performance degradation. Adaptive retraining frequency can be based on individual retention characteristics.Safety Guardrails Against Misuse

[0290] Context-aware safety limits can be used to prevent stimulation increases during movement or activity. Real-time symptom monitoring (tremor, dyskinesia detection) can be used with automatic safety intervention. Clinician-configurable safety boundaries can be used to override capabilities of the system with rate limiting on volitional stimulation changes to prevent rapid oscillations.Motor Performance Outcome

[0291] Outcomes of treatment can be assessed based on improvements, for example, in motor performance metrics (tapping speed, movement initiation latency) or reduced bradykinesia, as measured by standardized assessments. Systems can be configured to track and report motor outcome improvements over a training period.Table 2. The volitional BCI paradigm can be combined with multiple effector modalities for stroke and neurorehabilitation applications.Exoskeleton Gait training, upper limb rehab, SCI mobility, progressive resistance trainingRobotic Therapy End-effector robots, cable-driven systems, MIT-Manus type devices TMS / tDCS / tACS Non-invasive cortical modulation, priming for rehab, depression Focused Non-invasive deep brain targets, Ultrasound blood-brain barrier opening, neuromodulationSCS Pain, spasticity management, SCI motor recoveryCombination Central + peripheral (e.g., VNS +FES, DBS + exoskeleton)

Claims

What is claimed is:

1. A method for treating a neurological disorder or psychiatric disorder in a subject, the method comprising:positioning a neural recording electrode at a first location in or near a brain region of the subject to record neural signal data;positioning a stimulator to deliver stimulation to a second location in the subject; connecting a brain computer interface (BCI) to the neural recording electrode and a data receiving device, wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to the data receiving device, wherein the data receiving device comprises a processor coupled to a display;recording the neural signal data using the neural recording electrode while displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data; anddelivering stimulation to the second location in the subject using the stimulator, wherein the subject voluntarily increases or decreases the amplitude of the stimulation delivered to the second location in the subject by adjusting the vertical positioning of the graphical object on the display.

2. The method of claim 1 , wherein the graphical object resembles an airplane.

3. The method of claim 1 or 2, wherein the display further displays a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background.

4. The method of claim 3, wherein horizontal speed of the graphical object across the background is constant.

5. The method of any one of claims 1-4, further comprising training the subject to adjust amplitude of the stimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object on the display, wherein the subject adjusts the vertical positioning of the graphical object to reach a selected threshold.

6. The method of any one of claims 1-5, wherein the display further displays a target having a target shape, wherein said training comprises having the subject adjust amplitude of thestimulation by controlling the magnitude of the neural signal through adjusting the vertical positioning of the graphical object such that the graphical object hits the target on the display.

7. The method of claim 6, wherein the target is positioned at a position in a range from 25% to 75% of the height of the display.

8. The method of claim 6 or 7, wherein the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.

9. The method of any one of claims 1-8, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.

10. The method of any one of claims 1-8, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

11. The method of any one of claims 5-10, further comprising:disconnecting the BCI from the neural recording electrode and the data receiving device after said training; anddelivering stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.

12. The method of any one of claims 1-11, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

13. The method of any one of claims 1-12, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.

14. The method of any one of claims 1-13, further comprising positioning a stimulation electrode at the second location, wherein the stimulation electrode is connected to the stimulator.

15. The method of claim 14, wherein the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

16. The method of claim 14 or 15, wherein the stimulation electrode is positioned in a brain region, in a subgaleal space, on a head, in an epidural space, near a peripheral nerve, or near a muscle of the subject17. The method of any one of claims 1-16, wherein the stimulation comprises electrical stimulation, magnetic stimulation, or ultrasound stimulation.

18. The method of any one of claims 1-17, wherein the stimulation comprises deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.

19. The method of claim 18, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

20. The method of claim 17 or 18, wherein said delivering electrical stimulation comprises delivering electrical stimulation to a brain region, spinal cord, peripheral nerve, or muscle of the subject.

21. The method of claim 20, wherein the peripheral nerve is a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

22. The method of any one of claims 1-21, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.

23. The method of claim 22, wherein the movement disorder is Parkinson’s disease or dystonia.

24. The method of claim 23, wherein the first location is a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and the second location is a subthalamic nucleus brain region or a globus pallidus internus brain region.

25. The method of claim 23 or 24, wherein the neural signal is a cortical beta signal.

26. The method of claim 25, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.

27. The method of claim 25 or 26, wherein the vertical position of the graphical object on the display is controlled by magnitude of cortical beta power.

28. The method of claim 27, wherein the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.

29. The method of claim 27 or 28, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.

30. The method of claim 27 or 28, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.

31. The method of any one of claims 27-30, wherein said training comprises downregulating or up-regulating the cortical beta power by adjusting the vertical position of the graphical object on the display.

32. The method claim 31 , wherein said training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target.

33. The method of any one of claims 27-32, wherein the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold.

34. The method of any one of claims 27-32, wherein the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold.

35. The method of any one of claims 1-34, wherein the subject is required to remain immobile during the training.

36. The method of claim 35, further comprising:recording a video of the subject during the training; andchecking the video to determine if the subject remained immobile during the training.

37. The method of any one of claims 1-36, further comprising setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.

38. The method of any one of claims 1-37, wherein the psychiatric disorder is a mood disorder.

39. The method of any one of claims 1-38, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.

40. The method of any one of claims 1-39, wherein the stimulation is applied unilaterally or bilaterally.

41. The method of any one of claims 1-40, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.

42. The method of claim 41 , wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neuraloscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.

43. The method of any one of claims 1-42, wherein the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model.

44. The method of claim 43, wherein the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output.

45. The method of claim 44, wherein the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal.

46. The method of any one of claims 43-45, wherein the machine learning model uses multivariate analysis of the neural signal data.

47. The method of any one of claims 44-46, wherein the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection.

48. The method of any one of claims 44-47, wherein the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination.

49. The method of any one of claims 44-48, wherein the machine learning model uses dimension decomposition to isolate the biomarker from a confound.

50. The method of claim 49, wherein the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof.

51. The method of any one of claims 43-50, wherein the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker.

52. The method of any one of claims 43-51 , wherein the machine learning model is further used to distinguish movement artifacts from the neural signal.

53. The method of any one of claims 43-52, wherein the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data.

54. The method of any one of claims 43-53, wherein the machine learning model uses reinforcement learning threshold adaptation.

55. The method of any one of claims 43-54, wherein the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.

56. The method of any one of claims 1-55, wherein the data receiving device Is a computer, a cloud computing system, or a handheld device, optionally wherein the handheld device is a cell phone or tablet.

57. The method of any one of claims 1-56, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.

58. The method of claim 17, wherein the stimulator is an ultrasound transducer.

59. The method of claim 17, further comprising positioning a magnetic coil at the second location, wherein the magnetic coil is connected to the stimulator.

60. The method of any one of claims 1-59, further comprising automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject.

61. The method of claim 60, wherein the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.

62. The method of any one of claims 1-61, wherein said displaying the graphical object on the display comprises:displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; andsubsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time.

63. The method of claim 62, further comprising delivering stimulation using settings adjusted by the subject without displaying the graphical object on the display based on the training to achieve the learned neural state.

64. The method of claim 62 or 63, wherein the neural state is an induced neuroplasticity state or a patient-generated neural state.

65. The method of claim 64, wherein said delivering the stimulation induces the neuroplasticity state or the patient-generated neural state.

66. The method of claim 64 or 65, further comprising triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state.

67. The method of claim 66, wherein the assistive device is an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, or a language production device.

68. The method of claim 66 or 67, further comprising delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device.

69. The method of any one of claims 62-68, further comprising delivering transcranial stimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

70. The method of any one of claims 1-69, wherein the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.

71. The method of any one of claims 1-70, wherein said delivering the stimulation promotes survival of motor neurons, strengthens weakened cortico-spinal connections, improves function or re-establishes function of a neural pathway, or any combination thereof.

72. The method of any one of claims 1-71, wherein the neural signal data comprises motor neuron data, and wherein said delivering the stimulation comprises delivering stimulation to the central nervous system, peripheral nervous system, or combination thereof.

73. The method of claim 72, wherein said delivering the stimulation comprises vagus nerve stimulation, functional electrical stimulation, deep brain stimulation, or any combination thereof.

74. The method of any one of claims 1-74, further comprising detecting cortico-spinal connectivity changes, motor evoked potential changes, connectivity or neuroplasticity changes, or any combination thereof resulting from said delivering the stimulation.

75. The method of any one of claims 71-74, wherein the subject is recovering from a stroke.

76. The method of any one of claims 1-75, wherein the method is performed while the subject is at home with remote monitoring of the subject by a clinician, optionally wherein the clinician interacts with the subject through telemedicine or telehealth.

77. The method of claim 76, further comprising cloud-based data transmission of the neural signal data to a data receiving device used by the clinician.

78. The method of claim 77, further comprising remote parameter adjustment by the clinician.

79. The method of any one of claims 1-78, further comprising cloud-based data transmission to the data receiving device used by the subject of a computer program for displaying the graphical object on the display for training the subject to voluntarily increase or decrease the amplitude of the stimulation delivered to the second location in the subject by adjusting the vertical positioning of the graphical object on the display.

80. A computer-implemented method, the computer performing steps comprising: receiving recorded neural signal data from a neural recording electrode positioned at a first location in or near a brain region of the subject, wherein the neural recording electrode is connected to a brain computer interface (BCI) , wherein the BCI receives the neural signal data recorded by the neural recording electrode and transmits the neural signal data to a data receiving device comprising a processor coupled to a display;displaying a graphical object on the display, wherein vertical positioning of the graphical object on the display is controlled by magnitude of a neural signal in the neural signal data;adjusting settings of a stimulator based on the subject voluntarily increasing or decreasing the amplitude of the stimulation delivered to the subject by adjusting the vertical positioning of the graphical object on the display; andinstructing the stimulator to deliver the stimulation to the subject using the adjusted settings, wherein the stimulation is delivered to a second location in the subject.

81. The computer-implemented method of claim 80, wherein the graphical object resembles an airplane.

82. The computer-implemented method of claim 80 or 81, further comprising displaying a background comprising features moving from right to left to simulate movement of the graphical object horizontally across the background.

83. The computer-implemented method of claim 82, wherein horizontal speed of the graphical object across the background is constant.

84. The computer-implemented method of any one of claims 80-83, further comprising displaying a target having a target shape to provide training to the subject, wherein said training comprises having the subject adjust amplitude of the stimulation by controlling the magnitude of theneural signal through adjusting the vertical positioning of the graphical object such that the graphical object hits the target on the display.

85. The computer-implemented method of claim 84, wherein the target is positioned at a position in a range from 25% to 75% of the height of the display.

86. The computer- implemented method of claim 84 or 85, wherein the target shape is a spherical, circular, square, rectangular, triangular, or irregular shape.

87. The computer-implemented method of any one of claims 80-86, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.

88. The computer-implemented method of any one of claims 80-87, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

89. The computer-implemented method of any one of claims 80-88 further comprising instructing the stimulator to deliver the stimulation to the second location in the subject, wherein the subject voluntarily increases or decreases the amplitude of the stimulation without viewing the graphical object on the display.

90. The computer-implemented method of any one of claims 80-89, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

91. The computer-implemented method of any one of claims 80-90, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.

92. The computer-implemented method of any one of claims 80-91, wherein the stimulation comprises electrical stimulation, magnetic stimulation, or ultrasound stimulation.

93. The computer-implemented method of any one of claims 80-92, wherein the stimulation comprises deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.

94. The computer-implemented method of claim 93, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

95. The computer-implemented method of claim 92 or 93, wherein the electrical stimulation is delivered to a brain region, spinal cord, peripheral nerve, or muscle of the subject.

96. The computer-implemented method of claim 95, wherein the peripheral nerve is a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

97. The computer-implemented method of any one of claims 80-96, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.

98. The computer-implemented method of claim 97, wherein the movement disorder is Parkinson’s disease or dystonia.

99. The computer-implemented method of claim 98, wherein the first location is a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and the second location is a subthalamic nucleus brain region or a globus pallidus internus brain region.

100. The computer-implemented method of claim 98 or 99, wherein the neural signal is a cortical beta signal.

101. The computer-implemented method of claim 100, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.

102. The computer-implemented method of claim 100 or 101, wherein the vertical position of the graphical object on the display is controlled by magnitude of cortical beta power.

103. The computer-implemented method of claim 102, wherein the vertical position of the graphical object on the display is controlled by the magnitude of the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.

104. The computer-implemented method of claim 102 or 103, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.

105. The computer-implemented method of claim 102 or 103, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.

106. The computer-implemented method of any one of claims 100-105, wherein said training comprises down-regulating or up-regulating the cortical beta power by adjusting the vertical position of the graphical object on the display.

107. The computer-implemented method claim 106, wherein said training comprises a rest trial and a regulation trial, wherein during the rest trial, the subject observes natural fluctuations in the vertical position of the graphical object on the display but does not attempt to self-modulate the cortical beta power, and wherein during the regulation trial, the subject is required to down-regulate or up-regulate the cortical beta power in order to adjust the vertical position of the graphical object to hit the target.

108. The computer-implemented method of any one of claims 100-107, wherein the amplitude of the stimulation increases when the cortical beta power falls below the selected cortical beta power threshold.

109. The computer-implemented method of any one of claims 100-107, wherein the amplitude of the stimulation decreases when the cortical beta power falls below the selected cortical beta power threshold.

110. The computer-implemented method of any one of claims 80-109, wherein the subject is required to remain immobile during the training.

111. The computer-implemented method of claim 110, further comprising:Instructing a video recording device to record a video of the subject during the training; and analyzing the video to determine if the subject remained immobile during the training.

112. The computer-implemented method of any one of claims 80-111, further comprising setting an upper limit for the amplitude of the stimulation, wherein the upper limit is a maximal amplitude of the stimulation that does not cause the subject to experience distraction or discomfort.

113. The computer-implemented method of any one of claims 80-112, wherein the psychiatric disorder is a mood disorder.

114. The computer-implemented method of any one of claims 80-113, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.

115. The computer-implemented method of any one of claims 80-114, wherein the stimulation is applied unilaterally or bilaterally.

116. The computer-implemented method of any one of claims 80-115, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.

117. The computer-implemented method of claim 116, wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neural oscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.

118. The computer-implemented method of any one of claims 80-117, wherein the vertical position of the graphical object on the display is determined based on a neural signal classification output from a machine learning model.

119. The computer-implemented method of claim 118, wherein the machine learning model identifies one or more personalized biomarker features specific to the subject in the neural signal data to use in determining the neural signal classification output.

120. The computer-implemented method of claim 119, wherein the one or more personalized biomarker features are selected from peak frequency, channel, and phase of the neural signal.

121. The computer-implemented method of any one of claims 118-120, wherein the machine learning model uses multivariate analysis of the neural signal data.

122. The computer-implemented method of any one of claims 118-121, wherein the machine learning model is used to automate mapping of individual peak frequencies in the neural signal data for biomarker selection.

123. The computer-implemented method of any one of claims 118-122, wherein the machine learning model is further used to optimize the biomarker selection to maximize regulation versus rest discrimination.

124. The computer-implemented method of any one of claims 118-123, wherein the machine learning model uses dimension decomposition to isolate the biomarker from a confound.

125. The computer-implemented method of claim 124, wherein the machine learning model uses multimodal sensor fusion to identify the confound, optionally wherein the multimodal sensor fusion comprises using accelerometry data, electromyography (EMG) data, a medication log, or any combination thereof.

126. The computer-implemented method of any one of claims 118-125, wherein the machine learning model assesses the effects on the subject of medication state, sleep state, activity level, or any combination thereof on the biomarker.

127. The computer-implemented method of any one of claims 118-126, wherein the machine learning model is further used to distinguish movement artifacts from the neural signal.

128. The computer-implemented method of any one of claims 118-127, wherein the machine learning model automates threshold optimization for detecting the neural signal, optionally wherein the threshold is continuously or periodically recalibrated based on accumulated training data.

129. The computer-implemented method of any one of claims 118-128, wherein the machine learning model uses reinforcement learning threshold adaptation.

130. The computer-implemented method of any one of claims 118-129, wherein the machine learning model uses transfer learning to reduce calibration time for a subject using a population model.

131. The computer-implemented method of any one of claims 80-130, wherein the data receiving device Is a computer or a handheld device.

132. The computer-implemented method of claim 131, wherein the handheld device is a cell phone or tablet.

133. The computer-implemented method of any one of claims 80-132, wherein the data receiving device is a cloud computing system.

134. The computer-implemented method of any one of claims 80-133, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.

135. The computer-implemented method of any one of claims 80-134, further comprising automatically adjusting a threshold for the neural signal based on a BCI performance metric for the subject.

136. The computer-implemented method of claim 135, wherein the BCI performance metric is a target hit rate, a false discovery rate, or a signal-to-noise ratio between brain states.

137. The computer-implemented method of any one of claims 80-136, wherein said displaying the graphical object on the display comprises:displaying the graphical object on the display constantly during a first period of time for training the subject to adjust the amplitude of the stimulation delivered to the subject to achieve a learned neural state; andsubsequently displaying the graphical object on the display intermittently to provide intermittent feedback to the subject during a second period of time.

138. The computer-implemented method of claim 137, further comprising instructing the stimulator to deliver the stimulation to the subject using settings adjusted by the subject without displaying computer-implemented the graphical object on the display based on the training to achieve the learned neural state.

139. The computer-implemented method of claim 137 or 138, wherein the neural state is an induced neuroplasticity state or a patient-generated neural state.

140. The computer-implemented method of claim 139, wherein said delivering the stimulation induces the neuroplasticity state or the patient-generated neural state.

141. The computer-implemented method of claim 139 or 140, further comprising triggering actuation of an assistive device in response to the subject achieving the patient-generated neural state.

142. The computer-implemented method of claim 141, wherein the assistive device is an exoskeleton, an orthotic device, a robotic assistive device, a robotic prosthetic device, a robotic rehabilitation device, a muscular stimulation system, a spinal cord stimulation system, a room switch, a communication device, or a language production device.

143. The computer-implemented method of claim 141 or 142, further comprising delivering functional electrical stimulation, deep brain stimulation, vagus nerve stimulation, transcranial stimulation, or any combination thereof in addition to said triggering actuation of the assistive device.

144. The computer-implemented method of any one of claims 137-143, further comprising delivering transcranial stimulation when the subject is in the learned neural state, optionally wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

145. The computer-implemented method of any one of claims 80-144, wherein the display further displays therapeutic stimulation feedback regarding efficacy of the stimulation.

146. The computer-implemented method of any one of claims 80-145, wherein said delivering the stimulation promotes survival of motor neurons, strengthens weakened cortico-spinal connections, improves function or re-establishes function of a neural pathway, or any combination thereof.

147. The computer-implemented method of any one of claims 80-146, wherein the neural signal data comprises motor neuron data, and wherein said delivering the stimulation comprises delivering stimulation to the central nervous system, peripheral nervous system, or combination thereof.

148. The computer-implemented method of claim 147, wherein said delivering the stimulation comprises vagus nerve stimulation, functional electrical stimulation, deep brain stimulation, or any combination thereof.

149. The computer-implemented method of claim 147 or 148, wherein the subject is recovering from a stroke.

150. The computer-implemented method of any one of claims 80-149, further comprising detecting cortico-spinal connectivity changes, motor evoked potential changes, connectivity or neuroplasticity changes, or any combination thereof resulting from said delivering the stimulation.

151. The computer-implemented method of any one of claims 80-150, wherein the method is performed while the subject is at home with remote monitoring of the subject by a clinician, optionally wherein the clinician interacts with the subject through telemedicine or telehealth.

152. The computer-implemented method of claim 151, further comprising cloud-based data transmission of the neural signal data to a data receiving device used by the clinician.

153. The computer-implemented method of claim 152, further comprising remote parameter adjustment by the clinician.

154. The computer-implemented method of any one of claims 80-153, further comprising cloud-based data transmission of a computer program for performing the computer-implemented method to the data receiving device used by the subject.

155. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor in a computer, causes the processor to perform the method of any one of claims 80-154.

156. A kit comprising the non-transitory computer-readable medium of claim 155 and instructions for using the neurofeedback provided through the BCI to volitionally control stimulation for treatment of a neurological disorder or psychiatric disorder.

157. A system for treating a neurological disorder or psychiatric disorder in a subject, the system comprising:a neural recording electrode;a stimulator;a data receiving device comprising a processor programmed according to the computer implemented method of any one of claims 80-154;a display, wherein the display is connected to the data receiving device; anda brain computer interface (BCI), wherein the BCI is connected to the neural recording electrode and the data receiving device.

158. The system of claim 157, wherein the stimulator provides electrical stimulation, magnetic stimulation, or ultrasound stimulation.

159. The system of claim 158, wherein the electrical stimulation is applied unilaterally or bilaterally.

160. The system of any one of claims 157-159, wherein the stimulator provides deep brain stimulation, subgaleal electrical stimulation, transcranial stimulation, spinal cord stimulation, peripheral nerve stimulation, peripheral nerve field stimulation, neuromuscular electrical stimulation, or focused ultrasound stimulation.

161. The system of claim 160, wherein the transcranial stimulation is transcranial direct current stimulation, transcranial alternating current stimulation, or transcranial magnetic stimulation.

162. The system of claim 160, wherein the stimulator provides peripheral nerve stimulation or peripheral nerve field stimulation to a vagus nerve, a median nerve, an ulnar nerve, a radial nerve, an occipital nerve, a sphenopalatine ganglion, a trigeminal nerve, a tibial nerve, a scaral nerve, a hypoglossal nerve, a phrenic nerve, a greater auricular nerve, a peroneal nerve, a sciatic nerve, a pudendal posterior nerve, a saphenous nerve, a suprascapular nerve, a carotid sinus nerve, a cervical sympathetic chain nerve, or a auriculotemporal nerve.

163. The system of any one of claims 157-162, further comprising a video recording device.

164. The system of any one of claims 157-163, further comprising an external wearable monitor that can acquire accelerometry data, gyroscope data, magnetometer surface electromyographic (sEMG) data, or any combination thereof.

165. The system of any one of claims 157-164, wherein the data receiving device Is a computer or a handheld device.

166. The system of claim 165, wherein the handheld device is a cell phone or tablet.

167. The system of any one of claims 157-165, wherein the data receiving device is a cloud computing system.

168. The system of any one of claims 157-167, wherein the display is a liquid crystal display (LCD), light-emitting diode (LED) display, microLED display comprising microscopic LEDs, plasma (PDP) display, quantum dot LED (QLED) display, organic light emitting diode (OLED) display, quantum dot OLED (QD-OLED) display, or cathode ray tube display device.

169. The system of any one of claims 157-168, further comprising a stimulation electrode, wherein the stimulation electrode is connected to the stimulator.

170. The system of claim 169, wherein the stimulation electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

171. The system of any one of claims 157-170, wherein the neurological disorder is a movement disorder, a sleep disorder, stroke, epilepsy, a traumatic brain injury, dementia, delirium, coma or a minimally conscious state, or a pain-associated disorder.

172. The system of claim 171, wherein the movement disorder is Parkinson’s disease or dystonia.

173. The system of claim 172, wherein the neural recording electrode is adapted for positioning in or near a sensorimotor cortex brain region, a subthalamic nucleus brain region, a globus pallidus internus brain region, or a subgaleal region, and wherein the stimulation electrode is adapted for positioning in a subthalamic nucleus region or a globus pallidus internus region.

174. The system of any one of claims 157-173, wherein the vertical position of the graphical object on the display is proportional to log-transformed power of the neural signal.

175. The system any one of claims 157-173, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed power of the neural signal.

176. The system of any one of claims 157-175, wherein the neural signal comprises alpha frequency, beta frequency, gamma frequency, delta frequency, theta frequency, or mixed-frequency neural oscillations.

177. The system of claim 176, wherein the delta frequency neural oscillations are in a range from 0.5 Hz to 4 Hz, the theta frequency neural oscillations are in a range from 4 Hz to 7 Hz, the alpha frequency neural oscillations are in a range from 8 Hz to 12 Hz, the beta frequency neural oscillations are in a range from 13 Hz to 30 Hz, and the gamma frequency neural oscillations are in a range from 60 Hz to 90 Hz.

178. The system of claim 177, wherein the neural signal is a cortical beta signal.

179. The system of claim 178, wherein the cortical beta signal comprises beta oscillations in a range of 15 Hz to 20 Hz.

180. The system of claim 178 or 179, wherein the vertical position of the graphical object on the display is controlled by cortical beta power.

181. The system of claim 180, wherein the vertical position of the graphical object on the display is controlled by the cortical beta power of the primary motor cortex contralateral to the dominant hand of the subject.

182. The system of claim 180 or 181, wherein the vertical position of the graphical object on the display is proportional to log-transformed cortical beta power.

183. The system of claim 180 or 181, wherein the vertical position of the graphical object on the display is inversely proportional to log-transformed cortical beta power.

184. The system any one of claims 157-170, wherein the psychiatric disorder is a mood disorder.

185. The system any one of claims 157-184, wherein the psychiatric disorder is major depressive disorder, bipolar disorder, a disorder of diminished motivation, or obsessive-compulsive disorder.

186. The system of any one of claims 157-185, wherein the neural recording electrode is a non-brain penetrating surface electrode array or a brain-penetrating electrode array.

187. The system of any one of claims 157-186, wherein the neural recording electrode is an electroencephalogram (EEG) electrode array, a subgaleal or burrhole mounted or cranially mounted electrode, or an electrocorticogram (ECoG) electrode array.

188. The system of claim 157, wherein the stimulator is an ultrasound transducer.

189. The system of claim 157, further comprising a magnetic coil, wherein the magnetic coil is connected to the stimulator.

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