Systems and processes for closed-loop deep brain stimulation - Patents.com

Abstract

Systems, devices, and / or processes are provided that address the limitations of long-term stimulation with fixed parameters. [Solution] The method includes receiving neurophysiological activity data by a controller; receiving biometric data related to the patient by the controller; identifying one or more weighting components of the neurophysiological activity data by the controller; assigning a weight by the controller to each of the one or more weighting components based on the biometric data related to the patient; determining by the controller whether to apply DBS based on a trained algorithm applied to the one or more weighting components; and commanding the application of DBS by the controller based on the determination.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 298,896, filed January 12, 2022, the entirety of which is incorporated by reference herein.

[0002] The present disclosure relates to a system for closed-loop deep brain stimulation. The present disclosure further relates to a process for closed-loop deep brain stimulation. The present disclosure further relates to a system for closed-loop deep brain stimulation for stroke rehabilitation. The present disclosure further relates to a process for closed-loop deep brain stimulation for stroke rehabilitation. In accordance with aspects of the present disclosure, methods and systems are disclosed for identifying when and / or how to apply the application of deep brain stimulation (DBS) to a patient. [Background technology]

[0003] Electrical stimulation of the nervous system is an established method to provide functional and therapeutic benefits to patients across a variety of disease conditions. A common stimulation therapy is DBS, in which electrodes are placed at specific structures in the brain and electrical current is delivered to the tissue at the implantation site. The resulting neural activation provides therapeutic benefits to the patient.

[0004] Stimulation set points may be initially guided by the disease application, the device, and their location in the brain. Other parameters such as contact selection, stimulation amplitude, and frequency may further fine-tune the current delivery to achieve therapeutic benefit. Many DBS applications utilize chronic stimulation with fixed parameters. However, chronic stimulation with fixed parameters provides only limited benefit to the patient. Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, what is needed is a system, device, and / or process that addresses the limitations of long-term stimulation with fixed parameters. [Means for solving the problem]

[0006] The above needs are met to a large extent by the present disclosure.

[0007] In one general aspect, a method includes receiving, by a controller, neurophysiological activity data. The method additionally includes receiving, by the controller, biometric data related to a patient. The method further includes identifying, by the controller, one or more weighting components of the neurophysiological activity data, assigning, by the controller, a weight to each of the one or more weighting components based on the biometric data related to the patient, determining, by the controller, whether, when, or how to apply DBS based on a trained algorithm applied to the one or more weighting components, and commanding, by the controller, the application of DBS based on the determination.

[0008] In one general aspect, a system includes a controller configured to receive neurophysiological activity data. The system additionally includes the controller being further configured to receive biometric data related to the patient. The system further includes the controller being further configured to identify one or more weighting components of the neurophysiological activity data, the controller being further configured to assign a weight to each of the one or more weighting components based on the biometric data related to the patient, the controller being further configured to determine whether, when, and / or how to apply DBS based on a trained algorithm applied to the one or more weighting components, and the controller being further configured to command the application of DBS in response to the trained algorithm.

[0009] There have thus been outlined, in a relatively broad sense, certain aspects of the present disclosure in order that the detailed description of the present disclosure herein may be better understood, and in order that the present contributions to the art may be better appreciated. There are, of course, additional aspects of the present disclosure that will be described below and which will form the subject matter of the claims appended hereto.

[0010] In this regard, before describing at least one aspect of the present disclosure in detail, it is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is susceptible to aspects in addition to those described and can be practiced and carried out in various ways. It is also to be understood that the phraseology and terminology used in the specification, as well as in the abstract, are for the purpose of description and should not be regarded as limiting.

[0011] As such, those skilled in the art will appreciate that the conception on which the present disclosure is based may be readily utilized as a basis for the designing of other structures, methods and systems for carrying out some of the purposes of the present disclosure. It is important, therefore, that the claims be regarded as including such equivalent constructions insofar as they do not depart from the spirit and scope of the present disclosure. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 illustrates a system that can be used to configure a DBS system according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 illustrates exemplary details of a controller according to an aspect of the present disclosure. [Diagram 3] FIG. 1 is a diagram of a closed loop system according to an aspect of the present disclosure. [Figure 4A]FIG. 1 is a process flow diagram illustrating a method for determining whether, when, and / or how to apply DBS to treat a patient by stimulating the patient's brainstem, diencephalon, or cerebellar pathways connecting to the cerebrum in accordance with an embodiment of the present disclosure. [Figure 4B] FIG. 1 is a process flow diagram illustrating a method for determining whether, when, and / or how to apply DBS to treat a patient by stimulating the patient's brainstem, diencephalon, or cerebellar pathways connecting to the cerebrum in accordance with an embodiment of the present disclosure. [Figure 5A] 13 is a flow diagram for a frequency-based stimulation criteria process according to an aspect of the present disclosure. [Figure 5B] 13 is a flow diagram for a phase-based stimulation criteria process according to an aspect of the present disclosure. [Figure 5C] 1 is a flow diagram for a spike-based stimulation criteria process according to an aspect of the present disclosure. [Figure 5D] 1 is a flow diagram for time-locked neural signals as a criterion for a stimulation process according to an aspect of the present disclosure. [Figure 6] FIG. 1 illustrates an exemplary coordinated stimulation paradigm according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Aspects of the present disclosure relate to adaptive approaches that can better optimize stimulation to achieve functional and therapeutic benefits. By incorporating physiological activity into a closed loop system when guiding stimulation, pathological activity can be better targeted for elimination or to facilitate neural repair during application of DBS.

[0014] In this regard, post-stroke rehabilitation is one area that can achieve benefit from the application of DBS. Ischemic stroke remains a leading cause of disability, with few options for post-stroke treatment other than physical rehabilitation. Functional reconfiguration of sites adjacent to the stroke area (i.e., perilesional cortex) may be crucial to improve motor outcomes after a stroke has occurred. Functional reconfiguration of the perilesional cortex may occur in response to electrical stimulation. DBS can be used in post-stroke rehabilitation along with physical rehabilitation to induce increased neuroplasticity in the brain. Specifically, stimulation of the dentate-thalamocortical circuit via the dentate nucleus can enhance neuroplasticity, thereby aiding in functional reconfiguration of the perilesional cortex adjacent to the ischemic injury. In response to functional reconfiguration, post-stroke patients can experience motor improvements.

[0015] The promotion of neuroplasticity may depend on the timing and nature of the delivery of electrical stimulation. Continuous delivery of stimulation may not be ideal and may result in tissue fatigue or other treatment disorders. Furthermore, continuous electrical stimulation delivery may stimulate the brain at a time that is not timely or without benefit, without considering the adaptive properties resulting from the treatment, without considering the underlying neural activation. To achieve maximum reorganization of the perilesional cortex, it may be necessary to facilitate neuroplasticity at a precise time that enhances the benefit and avoids chronic flooding of cerebral and / or brain activity, fatigue, or habituation to the favorable stimulation benefits. Physiological and / or behavioral events can be used as a proxy for when the brain is most receptive to functional reorganization. These events can be used as a timed lock on stimulation to maximize improvement (i.e., closed loop). A closed loop system can have the ability to adapt stimulation parameters based on biofeedback of how the nervous system responds. Examples of stimulation parameters may include, but are not limited to, amplitude, pulse width, one or more frequencies, burst rate, burst count, phase orientation, voltage, current, on-time duration, off-time duration, number of pulses, and / or any combination thereof. Additionally, the closed loop system may narrow down neural activity for elimination and / or enhancement. The system may improve benefits through improved timing and avoid timing deficiencies that may be unbeneficial or detrimental to the rehabilitation process, as well as enable application in other disease modalities and rehabilitation situations.

[0016] The continuous delivery of DBS can be modified with biofeedback to improve therapeutic benefit. Biofeedback can include, for example, electrophysiological and / or mechanical feedback obtained by one or more means including electroencephalography (EEG), electromyography (EMG), electrocorticography (ECoG), motor function measures, and / or motor execution measures. A series of steps can be used to identify important stimulation benefit indicators for use in a closed-loop system. The DBS system can incorporate any number of these benchmarks, considering that certain criteria may be more appropriate for one patient compared to another, thus allowing flexibility in the weights and relationships between the metrics. Analysis of the patient's alertness level (i.e., active, cheerful, asleep), vocalization, motor activity (i.e., walking or moving arms), motor planning (i.e., brainwave activity consistent with planning upper limb movement or initiating walking), mood (measured by autonomy indicators, facial features, or other measures), and / or other biometric levels can indicate when the patient's brain is most amenable to rehabilitation approaches. Examples include adjusting the timing of stimulation relative to phases of motor planning, adjusting the timing of stimulation relative to phases of motor execution, and / or adjusting the timing of recording of neural activity.

[0017] The DBS systems disclosed herein may also be beneficial in cases of stroke, including traumatic brain injury, such as ischemic stroke and / or hemorrhagic stroke, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, and / or other neurological disorders.

[0018] The methods and systems disclosed herein can incorporate neurophysiological activity as a primary input metric into the algorithm that determines when to apply DBS to a patient. Neurophysiological activity can include, but is not limited to, local field potentials (i.e., electrocorticography, electroencephalography, and electromyography), single and multi-unit neural activity, heart rate, heart rate variability, and muscle responses, e.g., by electromyography. These signals can be further processed by a data acquisition system that can separate the relevant components of each signal for incorporation into the decision to apply electrical stimulation or not. DBS can be applied when the criteria for applying electrical stimulation are met. Additionally, stimulation can be initiated by user input through an external trigger that wirelessly communicates with an internal pulse generator to manually initiate it. A patient or clinician may be able to manually activate stimulation during active use of a rehabilitation device or activity at home or in a clinical setting.

[0019] FIG. 1 illustrates a system that can be used to construct a DBS system according to an embodiment of the present disclosure.

[0020] In particular, Figure 1 depicts a system 10 that can be used to configure a DBS system 200 to stimulate cerebellar pathways connecting to a patient's brainstem, diencephalon, and / or cerebrum, etc., to treat a neurological disorder in the patient. System 10 can be connected to, incorporated within, and / or controlled by DBS system 200, etc. Additionally, system 10 can be implemented as a stimulation determination system and / or a DBS control system, etc.

[0021] The system 10 may include a controller 12. The controller 12 may be configured to receive data from a patient's internal body portion 13. In certain aspects, the data from the internal body portion 13 may be acquired and / or recorded by one or more DBS electrodes 15. The controller 12 may be configured to receive data from a neurostimulator 14. The neurostimulator 14 may be inside the patient's body and / or outside the patient's body.

[0022] Additionally, the controller 12 can be configured to receive data from the patient's external body portion 16 from one or more EEG scalp electrodes 17 (electroencephalogram electrodes). In certain aspects, the received data can include spontaneous neural activity received while the patient is at rest. In other aspects, the received data can include data received in response to the patient performing a motor task using the task component 18. The controller 12 can receive data regarding the internal body portion 13 through a wired connection and / or a wireless connection implementing a communication channel as defined herein, etc.

[0023] Additionally, the controller 12 can be configured to receive data from the external portion 16 using a functional near-infrared spectroscopy (fNIRS) device 80. In certain embodiments, the fNIRS device 80 measures brain activity by using near-infrared light to estimate cortical hemodynamic activity that may occur in response to neural activity. In certain embodiments, the fNIRS device 80 implements and / or includes a light emitter and a light detector. The light emitter and light detector of the fNIRS device 80 can be positioned on the patient's skull. Additionally, the light emitter of the fNIRS device 80 can emit light and the light detector can sense the light from the light emitter to generate measurements for the system 10. In certain embodiments, the data from the external portion 16 further includes invasive recordings, such as data from an implanted electrocorticography (ECoG) strip and / or an electromyography device (not shown), which can be communicated to the controller 12.

[0024] In aspects, the intrabody portion 13 may be an operable system, device, hardware, etc., capable of at least partially implementing the controller 12. In aspects, the intrabody portion 13 may be an operable ECoG type system, device, hardware, etc., capable of at least partially implementing the controller 12.

[0025] The controller 12 may include one or more input / output devices 19. In certain embodiments, the one or more input / output devices 19 may be configured to provide instructions to a patient and / or medical professional in response to commands from the system 10 and / or the controller 12. In certain embodiments, the one or more input / output devices 19 may be configured to provide output configurations for the DBS system 200. In certain embodiments, the one or more input / output devices 19 may be configured to control the system 10, the controller 12, the DBS system 200, etc.

[0026] In certain aspects, the one or more input / output devices 19 can include buttons, soft keys, a mouse, a voice-activated control, a touch screen, a keyboard, a speaker, a microphone, a camera, and / or audio notifications, etc. The one or more input / output devices 19 can be configured to provide output from the system 10, the DBS system 200, and / or the controller 12, etc., through a graphical user interface that includes visual notifications.

[0027] The internal portion 13 may be implanted within the patient's body with one or more DBS electrodes 15 in the patient's brain, for example in contact with or adjacent to the dentate nucleus, and the neurostimulator located remote from the brain (external to the patient's body or implanted under the patient's skin). In some embodiments, the external portion 16 is not implanted within the patient's body. In aspects, the internal portion 13 may include other implantable hardware, such as ECoG, depth electrodes, and / or other contacts on a DBS lead. Data from the implantable hardware may form part of the feedback described herein.

[0028] Although the one or more EEG scalp electrodes 17 are shown as a plurality of electrodes, it should be understood that the one or more EEG scalp electrodes 17 includes any number of implementations of one or more EEG scalp electrodes 17 limited by the size of the patient's head and more than 1. For example, the number of implementations of one or more EEG scalp electrodes 17 can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 electrodes, or more than 30 electrodes, as desired. The one or more EEG scalp electrodes 17 may be arranged in a 10-5 layout, a 10-10 layout, a 10-20 layout, or a similar layout.

[0029] 1, for example, controller 12, may be equipped with a non-transitory memory that stores instructions (and in some cases instance data) for configuration, and a processor that accesses and executes the instructions. The non-transitory memory and processor may be implemented as a single circuit, such as an application specific integrated circuit (ASIC), but may be any possible implementation of a non-transitory memory and associated processor. In certain aspects, one or more input / output devices 19 may be implemented as input devices, such as a mouse and / or keyboard, and may be components of controller 12 that enable interaction with controller 12 or any other component of system 10.

[0030] The controller 12 may engage in wired and / or wireless communication. For example, the controller 12 may communicate with a neurostimulator 14 implanted in an internal portion 13 of the patient's body according to short-range wireless communication means (with any necessary additional circuitry not illustrated). One or more EEG scalp electrodes 17 may be connected to the controller 12 for engaging in wired or wireless communication (by means that may not be illustrated). The controller 12 may be connected to a challenge component 18 and / or one or more input / output devices 19 according to wired or wireless connections.

[0031] The task component 18 can be one or more instruments configured to measure one or more mechanical attributes as the user performs the task they are instructed to perform. In certain aspects, instructions to perform the task are provided by a physician, generated by the system 10, generated by the controller 12, and / or displayed on one or more input / output devices 19, etc.

[0032] By way of example, the task component 18 may provide mechanical or digital measurements of movement and may include dynameters, digital plates, articulated levers, robotic arms, other mechanical or digital measurement devices. The movement measurements may include, for example, limb or body part displacement / velocity / acceleration, or limb or body part dexterity, strength, resistance (stiffness or spasticity), and / or electromyography results, etc. In certain aspects, the task component 18 may measure movement and provide data regarding the movement to the system 10 and / or controller 12, etc.

[0033] The system 10 can be used to configure the DBS system 200 to stimulate cerebellar pathways connecting to the brainstem, diencephalon, cerebrum, or other locations in the patient's brain to treat a neurological disorder in the patient. The controller 12 can perform steps associated with configuring the DBS system 200. Configuring the DBS system 200 can include one or more of: electrical stimulation of any components of the neural pathways associated with a neurological condition, in vivo recording of electrophysiology of subcortical sites and / or deep brain tissue, in vivo recording of transmission from primary motor cortex, secondary motor cortex, primary sensory cortex, and / or secondary sensory cortex, and mechanical measurements when performing or attempting to perform at least one task using the task components, including a motor task, a speech task, a cognitive task, and / or a combination of one or more of these. For example, system 10 can be used to perform the closed-loop deep brain stimulation method 40 (FIGS. 4A and 4B) described below (or any other process for configurations using different combinations of mechanical measures when performing or attempting to perform at least one task, including electrical stimulation of any component of a neural pathway related to a neural condition, in vivo recording of electrophysiology of subcortical sites and / or deep brain tissue, ex vivo recording of transmission from primary motor cortex, secondary motor cortex, primary sensory cortex, and / or secondary sensory cortex, motor tasks, vocal tasks, and / or cognitive tasks, etc.).

[0034] The DBS system 200 can include an implantable pulse generator (IPG). The IPG can be a neurostimulator and can be configured to deliver electrical pulses to the patient's brain. The DBS system 200 can include one or more implanted DBS electrodes 15 that can be placed at multiple sites on the patient's brain. The DBS system 200 can be configured such that leads can be connected to the IPG.

[0035] FIG. 2 illustrates exemplary details of a controller according to an embodiment of the present disclosure.

[0036] 2, the controller 12 may include a non-transitory memory 22 for storing instructions, data, and the like. For example, the non-transitory memory 22 may store instructions for executing a closed-loop deep brain stimulation method 40 described later in this specification. For example, the non-transitory memory may be a read-only memory (ROM), a random access memory (RAM), a magnetic RAM, a core memory, a magnetic disk storage medium, an optical storage medium, a flash memory device, and / or other machine-readable (in other words, readable by a processor) medium for storing information including instructions and / or data.

[0037] The non-transitory memory 22 can be connected to a receiver 26. The receiver 26 can receive data, such as from the internal portion 13, the external portion 16, the task component 18, and / or one or more input / output devices 19. The receiver 26 can receive signals from the internal portion 13 and the external portion 16, including internal data (e.g., electrophysiological data) and external data (e.g., EEG data). In some cases, the receiver 26 can receive data from the task component 18, such as information regarding one or more mechanical attributes when performing a task that the user has been instructed to perform. In some aspects, the one or more input / output devices 19 can receive input from a clinician, such as limitations and / or constraints on the operation of the controller 12.

[0038] In addition, the controller 12 may include a processor 24. The processor 24 may be configured to execute various aspects of the system 10. The processor 24 may further be configured to execute a closed-loop deep brain stimulation method 40, as further described herein. The processor 24 may be coupled to an output 28. The processor 24 may provide an output (including configurations and / or challenges, etc.) to the output 28 using at least a portion of the received data. The output 28 may provide an output to one or more input / output devices 19, which may provide audio and / or visual output.

[0039] FIG. 3 is a diagram of a closed loop system according to an embodiment of the present disclosure.

[0040] In particular, FIG. 3 is a diagram of a closed-loop system 30. In contrast to continuous stimulation, the decision to deliver electrical stimulation by the DBS system 200 and / or the manner in which electrical stimulation is delivered by the DBS system 200 may be determined based on a closed-loop approach. In certain aspects, the at least one neural signal 31 and / or the at least one peripheral signal 33 may be input into a data acquisition platform for signal processing 35. The data acquisition platform may be implemented by the system 10. The at least one neural signal 31 may include, but is not limited to, local field potentials (LFPs) recorded from an electroencephalogram (EEG), signals from electrocorticography (ECoG), and / or signals from another suitable device. The at least one peripheral signal 33 may include, but is not limited to, outputs from an electromyography (EMG), single or multi-unit activity, heart rate, heart rate variability, muscle responses, measures of speech function such as phonation, gait dynamics, and / or any other physiological aspects of the patient.

[0041] At least one neural signal 31 and at least one peripheral signal 33 can be processed by a data acquisition platform in signal processing 35. Here, signal processing techniques are implemented to isolate relevant activity contained in the acquired data. Extracted information from the signals can include average response profile, instantaneous power, power over time, distribution of signal frequencies, phase content, patient condition (i.e., active, energetic, sleep state, etc.), and / or ambulatory activity level.

[0042] In certain aspects, the extracted information may be compared to a criteria set 37. If the criteria set 37 for applying stimulation are satisfied, then the system 10 proceeds by applying DBS 39. If the criteria set 37 are not satisfied, then the closed-loop system 30 will not apply DBS.

[0043] In yet another aspect, the closed loop system 30 can be capable of adapting one or more stimulation parameters for the DBS 39. The stimulation parameters may be based on biofeedback of how the nervous system responds and / or neurophysiological data to establish stimulation parameters related to ongoing neural activity, etc. In other words, the closed loop system 30 can be capable of determining how to apply the DBS 39. For example, the system 10, the controller 12, and / or the signal processing 35, etc. can generate stimulation parameters 38. The system 10 would then proceed by applying the DBS 39 based on the stimulation parameters 38. In this case, again, examples of stimulation parameters can include DBS amplitude, DBS voltage, DBS current, DBS frequency, DBS on-time, DBS off-time, DBS pulse count, DBS pulse width, one or more DBS frequencies, DBS burst rate, DBS burst count, and / or DBS phase orientation, etc.

[0044] 4A and 4B are process flow diagrams illustrating a method for determining when and / or how to apply DBS to treat a patient by stimulating the patient's brainstem, diencephalon, or cerebellar pathways connecting to the cerebrum in accordance with an embodiment of the present disclosure.

[0045] In particular, Figures 4A and 4B illustrate a closed-loop deep brain stimulation method 40 that may be performed using the system 10 (another embodiment of which is shown in Figure 2) shown in Figure 1, the controller 12, and / or the DBS system 200, etc. Although the closed-loop deep brain stimulation method 40 is shown and described as being performed sequentially, it is understood and appreciated that the disclosure of the present invention is not limited by the order of the illustrated closed-loop deep brain stimulation method 40, as it is contemplated that some steps may occur in a different order and / or simultaneously with other steps shown and described herein. In addition, not all of the illustrated aspects may be required to perform the closed-loop deep brain stimulation method 40, and the closed-loop deep brain stimulation method 40 may not necessarily be limited to the aspects shown. Furthermore, one or more of these steps may be stored in a non-transitory memory, accessed, and executed by a processor.

[0046] As an optional first step (not shown), an initial monopolar workup (or monopolar electrical stimulation) can be performed to determine any electrodes and / or stimulation parameters that result in undesirable side effects. Such information can be entered by the clinician through one or more input / output devices 19 to constrain the parameters to those that are not harmful for the particular patient. Those electrodes and / or stimulation parameters that result in undesirable side effects can be excluded from further steps of the closed-loop deep brain stimulation method 40. The decision to exclude can be user specific (e.g., based on symptoms and / or the manner in which the electrodes are implanted). However, the decision to exclude can be based on (or supplemented by) population specific data that includes at least one similar patient.

[0047] At step 42 in the closed-loop deep brain stimulation method 40 of FIG. 4B, the patient may be instructed to perform one or more tasks, including one or more motor tasks, speech tasks, cognitive tasks, and the like. For example, the patient may be instructed by a medical professional to perform one or more tasks (e.g., motor tasks, speech tasks, cognitive tasks, or the like). In certain embodiments, the one or more motor tasks may be selected from a predefined list based on the patient's condition. In embodiments, step 42 may include acquiring data regarding the one or more tasks. In embodiments, step 42 may include acquiring data regarding how the stimulation affects spontaneous neural data as a separate option from the task-related changes. In embodiments, step 42 may include acquiring behavioral data. This data may be received from one or more devices and / or from a clinician through one or more input / output devices 19.

[0048] As another example, the system 10 and / or controller 12 can determine one or more tasks. In certain aspects, the system 10 and / or controller 12 can determine one or more tasks (e.g., motor tasks, speech tasks, and / or cognitive tasks, etc.) based on data entered therein about the patient, the patient's past function, and / or a population of similar patients, etc. The system 10 and / or controller 12, etc. can then output instructions regarding the one or more tasks (e.g., motor tasks, speech tasks, and / or cognitive tasks, etc.) via one or more input / output devices 19, audio output devices, and / or video devices, etc.

[0049] In another example, system 10 can instruct the patient to perform a task and record associated changes. Thus, the patient can perform, at least attempt to perform, plan to perform, and / or contemplate performing one or more tasks (e.g., tasks such as motor, vocal, and / or cognitive tasks), while receiving (by controller 12) internal data (e.g., electrophysiological data used in step 44) and / or external data (e.g., electroencephalography (EEG) data used in step 46) recorded by suitable electrodes. In some cases, the tasks (e.g., tasks such as motor, vocal, and / or cognitive tasks) can be assisted by task component 18 of FIG. 1, which can record data associated with the motor tasks.

[0050] Additionally, although described with respect to the patient performing or attempting to perform the same task (e.g., a motor task, a vocal task, and / or a cognitive task, etc.), it will be appreciated that these steps can be performed with multiple tasks (e.g., a motor task, a vocal task, and / or a cognitive task, etc.), which can be the same or different. For example, a motor task can include moving an affected limb, such as an arm, a hand, a finger, a foot, and / or a leg. In some patients, different parts of the same limb may be affected and / or different limbs may be affected. Similarly, an unaffected limb can be the site of a task. Alternatively, natural movements of the unaffected limb can be detected by the system 10 and / or the task component 18 to guide motor planning, purposes of detecting motor execution, programming and / or stimulus delivery. Vocal tasks can include steps such as repeating sounds or words, reciting sentences, and / or changing tone of voice. Cognitive tasks can include one or more memory tasks, calculation tasks, and / or other cognitive functions.

[0051] At step 44, neurophysiological activity data may be received by the data acquisition platform. The neurophysiological data may include at least one neural signal 31 and / or peripheral signal 32, and may include signals from an electroencephalogram (EEG), electrocorticography (ECoG), local field potentials (LFP) recorded from other suitable devices, outputs from electromyography (EMG), and / or single or multi-unit activity, heart rate, heart rate variability, and / or muscle responses, etc.

[0052] In step 46, the neurophysiological activity data can be processed. From the neurophysiological activity data, patient-specific data can be isolated using signal processing techniques. From these data, relevant information can be extracted, such as average response profile, instantaneous power, power over time, distribution of signal frequencies, phase components, patient condition (i.e., active, energetic, sleep state, etc.), and ambulatory activity level. The relevant information can be assigned different weights based on the patient or rehabilitation treatment. The processed signals and the weights assigned thereto can be input into an algorithm trained to determine the outcome of whether to apply DBS, whether to not apply BS, when to apply DBS, and / or how to apply DBS.

[0053] The algorithm may be implemented using artificial intelligence as defined herein. For example, the algorithm may be a convolutional neural network, a heuristic algorithm, and / or any other suitable algorithm, and may be trained to identify various criteria, such as different levels of arousal or aspects of motor planning, that indicate whether, when, and / or how to apply DBS.

[0054] The algorithm can be trained to identify criteria that reflect any other relevant benchmarks from the input signals for applying DBS, such as frequency, phase, or single- or multi-unit activity of the input neural or peripheral signals.

[0055] The algorithm can also be trained using a sample data set from a single patient to differentiate relevant biomarker benchmarks (i.e., whether the patient's eyes are open or closed, whether the patient is awake or asleep) to determine criteria for whether, when, and / or how to apply DBS. The algorithm can be less computationally intensive, allowing smaller computing devices to be used with the DBS system 200. Alternatively, the algorithm can be trained to determine when to stop stimulation in the case of continuous inputs not related to task performance, for example, when the patient is resting.

[0056] In certain aspects, an algorithm may utilize any of the input signals to determine how to apply DBS by the DBS system 200, or in other words, determine stimulation parameters for the application of DBS by the DBS system 200. For example, the algorithm may determine DBS amplitude, DBS voltage, DBS current, DBS frequency, DBS on-time, DBS off-time, DBS pulse number, DBS pulse width, DBS frequency(es), DBS burst rate, DBS burst count, and / or DBS phase orientation, etc.

[0057] In certain aspects, an algorithm may utilize any of the input signals to determine when to apply DBS by the DBS system 200. For example, the algorithm may determine the timing of DBS application with respect to a particular neural signal, a particular motor, speech, or cognitive task, a pulse wave, and / or a recent DBS application, etc.

[0058] 5A-5D are exemplary embodiments of various criteria as a benchmark for the application of DBS. Additional criteria to the benchmark can be used to determine whether to apply DBS.

[0059] FIG. 5A is a flow diagram for frequency-based stimulation criteria according to an embodiment of the present disclosure.

[0060] In particular, FIG. 5A is a flow diagram for a frequency-based stimulation criteria process 50A. The data acquisition platform or signal processing 35 can analyze one or more frequency components 51 from at least one neural signal 31 and / or peripheral signal 32 as criteria for stimulation. For example, if the strength of the first component is weaker than the subsequent components, the benchmark range can be applied, and DBS is applied in application 52A. If the strength of the first component is stronger than the subsequent components, DBS is not applied in no action phase 52B. Instantaneous power, as well as the distribution of frequency components and / or changes in power associated with time, are examples where these data can be used as input metrics.

[0061] FIG. 5B is a flow diagram for phase-based stimulation criteria according to an embodiment of the present disclosure.

[0062] In particular, Figure 5B is a flow diagram for a phase-based stimulation criteria process 50B. The data acquisition platform or signal processing 35 can process one or more phase components from at least one neural signal 31 as criteria for stimulation 53. The timing stimulation may be based on the phase of a particular frequency band (or range) that increases or decreases the resulting response. Avoidance of certain phases within certain frequency bands may be intended.

[0063] FIG. 5C is a flow diagram for spike-based stimulation criteria according to an embodiment of the present disclosure.

[0064] In particular, FIG. 5C is a flow diagram for a spike-based stimulation criteria process 50C. The decision to stimulate can incorporate single and / or multi-unit activity from cells recorded in the central or peripheral nervous system input by at least one neural signal 31 into the signal processing 35. A phase-signal channel 54 can then determine stimulation 56A. A phase coincidence (multiple channels) 55 can determine either stimulation 56A or no action 56B. The criteria can be based on individual unit activity or coincidence of multiple cells. In addition to this, the phase of cell firing can be used as yet another determining factor as to when to stimulate or avoid.

[0065] FIG. 5D is a flow diagram for time-locked neural signals as a criterion for a stimulation process according to an embodiment of the present disclosure.

[0066] In particular, Figure 5D is a flow diagram for a time-locked neural signal as a criterion for a stimulation process 50D. Activity such as a cortical evoked potential averaged and time-locked to the onset of a previous stimulation pulse can serve as an input metric to signal processing 35 as at least one neural signal 31. Power, frequency content, and / or other computer metrics 57 of the time-locked neural signal can be input into signal processing 35 and into an algorithmic decision to stimulate or no action in stimulation 58A 58B.

[0067] FIG. 6 is a co-stimulation paradigm according to an embodiment of the present disclosure.

[0068] In particular, Figure 6 is a coordinated stimulation paradigm in which a button can activate a wireless transmitter 62 that can be used to trigger stimulation. The wireless transmitter 62 can be paired with an implantable pulse generator (IPG) 63 that applies stimulation to a patient 64. The IPG can provide stimulation in the absence of physiological input or even longer term (i.e., continuous) or can utilize specific algorithms to communicate with a rehabilitation device 65. Additionally, the patient 64 can be tasked with using the rehabilitation device 65 to complete rehabilitation tasks while at home or within a clinical setting.

[0069] Thus, system 10 and / or closed-loop deep brain stimulation method 40 may provide an adaptive approach that may better optimize stimulation in achieving functional and therapeutic benefits. In this regard, system 10 and / or closed-loop deep brain stimulation method 40 may incorporate physiological activity into a closed-loop system in guiding stimulation, thereby better targeting pathological activity for elimination or facilitating neural repair with DBS application.

[0070] Additionally, the system 10 and / or closed-loop deep brain stimulation method 40 can improve neuroplasticity by controlling the timing and nature of electrical stimulation delivery by the DBS system 200. Continuous stimulation delivery may not be ideal and may result in tissue fatigue or other treatment impairments. Furthermore, continuous electrical stimulation delivery does not accommodate the adaptive properties resulting from the treatment, does not take into account underlying neural activation, and may stimulate the brain at an inopportune or non-beneficial time.

[0071] In addition, the system 10 and / or closed-loop deep brain stimulation method 40 can achieve maximum reconfiguration of the perilesional cortex by facilitating neuroplasticity at precise times that enhance benefits and avoid chronic flooding of the cortex, fatigue, or habituation to the preferred stimulation benefits. The system 10 and / or closed-loop deep brain stimulation method 40 can use physiological and / or behavioral events as proxies for when the brain is most amenable to functional reconfiguration. These events can be used as a timed lock on stimulation to maximize improvement (i.e., closed loop).

[0072] In addition, the system 10 and / or the closed-loop deep brain stimulation method 40 may have the ability to adapt stimulation parameters based on biofeedback of how the nervous system responds. Furthermore, the system 10 and / or the closed-loop deep brain stimulation method 40 may narrow down neural activity for elimination and / or enhancement. The system 10 and / or the closed-loop deep brain stimulation method 40 may improve benefits through improved timing and avoid timing deficiencies that may be unbeneficial or detrimental to the rehabilitation process, as well as enable application in other disease modalities and rehabilitation situations.

[0073] In addition, the system 10 and / or the closed-loop deep brain stimulation method 40 can use biofeedback to modify the continuous delivery of DBS so that therapeutic benefits can be improved. Furthermore, the system 10 and / or the closed-loop deep brain stimulation method 40 can execute a series of steps to identify stimulation benefit indicators that are important for use in a closed-loop system. The system 10 and / or the closed-loop deep brain stimulation method 40 can incorporate any number of these benchmarks, considering that certain criteria may be more appropriate for one patient compared to another, thus allowing flexibility in the weights and relationships between the metrics. The system 10 and / or the closed-loop deep brain stimulation method 40 can analyze the patient's alertness level (i.e., active, cheerful, sleep state), vocalization, or other biometric levels to determine when the patient's brain is most amenable to rehabilitation techniques. Furthermore, the system 10 and / or the closed-loop deep brain stimulation method 40 can adjust the timing of stimulation relative to phases of motor planning and / or to phases of motor execution.

[0074] Additionally, the system 10 and / or closed-loop deep brain stimulation method 40 may be beneficial in the case of ischemic stroke, traumatic brain injury, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, and / or other neurological disorders.

[0075] Accordingly, this disclosure recites implementations of system 10 and / or closed-loop deep brain stimulation method 40 to address the limitations of long-term stimulation with fixed parameters.

[0076] Below are some non-limiting examples of aspects of the present disclosure.

[0077] One embodiment is a method that includes receiving, by a controller, neuro-physiological activity data. The method further includes receiving, by the controller, biometric data related to a patient. The method further includes identifying, by the controller, one or more weighting components of the neuro-physiological activity data, assigning, by the controller, a weight to each of the one or more weighting components based on the biometric data related to the patient, determining, by the controller, whether to apply DBS based on a trained algorithm applied to the one or more weighting components, and commanding, by the controller, the application of DBS based on the determination.

[0078] The above-mentioned embodiment may further include a combination of any one or more of the following embodiments, i.e., the method of the above-mentioned embodiment may include receiving feedback data by the controller following DBS, training an algorithm based on the feedback data and determining by the controller whether a sufficient criteria level is satisfied to stimulate DBS, and commanding application of DBS by the controller based on the determination. The method of the above-mentioned embodiment, wherein commanding application may include adjusting timing of stimulation relative to one or more phases of motor planning and / or one or more phases of motor execution. The method of the above-mentioned embodiment. The method of the above-mentioned embodiment, wherein the neurophysiological activity data includes at least one of local field potentials, single and multi-unit neural activity, heart rate, heart rate variability, and / or muscle response. The method of the above-mentioned embodiment, wherein the biometric data relating to the patient includes at least one of the patient's state of consciousness and / or the patient's activity level. The method of any of the above embodiments, wherein determining whether to apply DBS may include identifying at least one benchmark associated with at least one of the following: frequency of the received neurophysiological data, phase of a frequency band of the received neurophysiological data, spikes of individual unit activity, phase coincidence of multi-unit activity of the received neurophysiological data, and / or a time-locked neural signal. The method of any of the above embodiments may include instructing the patient to complete a task. The method of any of the above embodiments, wherein the task may include one or more motor tasks. The method of any of the above embodiments, wherein the one or more motor tasks may include moving the affected limb. The method of any of the above embodiments, wherein the trained algorithm may include one or both of a convolutional neural network algorithm and a heuristic algorithm. The method of any of the above embodiments, wherein biometric data is collected when the patient is instructed to complete the task, when they are in the process of completing the task, and / or when they have completed the task. The method of any of the above embodiments, wherein the patient has one or more conditions including ischemic stroke, traumatic brain injury, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, or other neurological disorder.

[0079] One embodiment includes a system including a controller configured to receive neurophysiological activity data, the system further including the controller further configured to receive biometric data related to the patient, the system further including the controller further configured to identify one or more weighting components of the neurophysiological activity, the controller further configured to assign a weight to each of the one or more weighting components based on the biometric data related to the patient, the controller further configured to determine whether to apply DBS based on a trained algorithm applied to the one or more weighting components, and the controller further configured to command the application of DBS in response to the trained algorithm.

[0080] The above-mentioned embodiment may further include a combination of any one or more of the following embodiments, i.e., the system of the above-mentioned embodiment may include the controller being further configured to receive feedback data following DBS, the controller being further configured to train an algorithm based on the feedback data and determine whether a sufficient criteria level is satisfied to stimulate DBS, and the controller being further configured to apply DBS based on the determination.The system of the above-mentioned embodiment, wherein the controller is further configured to adjust the timing of stimulation relative to one or more phases of the exercise planning or one or more phases of the exercise execution.The system of the above-mentioned embodiment, wherein the neurophysiological activity data includes at least one of local field potentials, single and multi-unit neural activity, heart rate, heart rate variability, and / or muscle response.The system of the above-mentioned embodiment, wherein the biometric data related to the patient includes at least one of the patient's state of consciousness and / or the patient's activity level. The system of any of the above embodiments, wherein the controller is further configured to determine whether to apply DBS may include identifying at least one benchmark associated with at least one of: a frequency of the received neurophysiological data, a phase of a frequency band of the received neurophysiological data, spikes of individual unit activity, a coincidence of phases of multi-unit activity of the received neurophysiological data, and / or a time-locked neural signal.The system of any of the above embodiments, wherein the controller is further configured to instruct the patient to complete a task.The system of any of the above embodiments, wherein the task may include one or more motor tasks.The system of any of the above embodiments, wherein the one or more motor tasks may include moving an affected limb.The system of any of the above embodiments, wherein the trained algorithm may include one or both of a convolutional neural network algorithm and a heuristic algorithm.The system of any of the above embodiments, wherein the controller is further configured to collect biometric data when the patient is instructed to complete the task, is in the process of completing the task, or has completed the task.The system of any of the above embodiments, wherein the patient has one or more conditions including ischemic stroke, traumatic brain injury, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, or other neurological disorder.

[0081] It will be understood that although terms such as first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element could be called a second element, and a second element could be called a first element, without departing from the scope of the present disclosure. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0082] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure of the present invention. As used herein, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise. It will be further understood that the terms "comprise," "comprising," "includes," and / or "including" as used herein specify the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0083] Unless otherwise specified, all terms (including scientific and technical terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that the terms used herein should be interpreted to have a meaning consistent with the meaning of these terms in the context of this specification and the related art, and that idealized or overly public meanings are not to be interpreted unless expressly set forth herein.

[0084] Aspects of the present disclosure may include, for example, a wired / wireless local area network (LAN), a wired / wireless personal area network (PAN), a wireless LAN, a wireless personal area network (WLAN), a wireless personal area network (PLAN), a wireless personal area network (PAN), a wireless personal area network (PAN), a wireless personal area network (PAN), a wireless personal area network (PAN), a wireless personal area network (WLAN), a wireless personal area network (WPA ... The NFC standard may include a communication channel that may be any type of wired or wireless electronic communication network, such as a Personal Area Network (PAN), a wired / wireless home area network (HAN), a wired / wireless wide area network (WAN), a campus network, a metropolitan network, an enterprise private network, a virtual private network (VPN), an internetwork, a backbone network (BBN), a global area network (GAN), the Internet, an intranet, an extranet, an overlay network, a near field communication (NFC), a cellular telephone network, or a personal communication service (PCS). The NFC standard is intended to be a communication protocol and a data exchange format, and is based on existing radio frequency identification (RFID) standards, including ISO / IEC 14443 and FeliCa (registered trademark). The standards include ISO / IEC 18092 [3] and those defined by the NFC Forum.

[0085] The present disclosure may be implemented on any type of computing device, such as, for example, a desktop computer, a personal computer, a laptop / mobile computer, a personal digital assistant (PDA), a mobile phone, a tablet computer, and a cloud computing device having wired / wireless communication capabilities over a communication channel.

[0086] It should also be noted that the software implementations of the present disclosure described herein are optionally stored on a tangible storage medium, such as a magnetic medium, such as a disk or tape, a magneto-optical or optical medium, such as a disk, or a solid state medium, such as a memory card or other package that stores one or more read-only (non-volatile) memories, random access memories, or other rewritable (volatile) memories. A distribution medium equivalent to a tangible storage medium is considered to be a digital file attachment to an email or other self-contained information archive or set thereof. Thus, the present disclosure is considered to include any tangible storage medium or distribution medium, including art-recognized equivalents and successor media enumerated herein on which the software implementations of the present disclosure may be stored.

[0087] In addition, various aspects of the present disclosure may be implemented in non-general purpose computer implementations. Moreover, various aspects of the present disclosure as disclosed herein improve the functionality of a system as is evident from the present disclosure set forth herein. Moreover, various aspects of the present disclosure include computer hardware that is specially programmed to solve the complex problems addressed by the present disclosure. Thus, various aspects of the present disclosure improve the overall functionality of a system for carrying out the processes as disclosed by the present disclosure and as defined by the claims.

[0088] The artificial intelligence and / or machine learning may utilize any number of techniques including one or more of convolutional neural networks, heuristic algorithms, cybernetics and brain simulation, symbolic, cognitive simulation, logic-based, anti-logic, knowledge-based, sub-symbolic, embodied intelligence, computational intelligence and soft computers, and / or machine learning and statistics, etc.

[0089] Various aspects of the system and method may utilize speech recognition software. A user may be able to utilize speech instead of utilizing other input processes. For example, speech recognition software may be configured to generate text from voice input from a microphone or other audio input, etc. A speech signal processor may convert speech signals into digital data that may be processed by a processor. The processor may perform several distinct functions, including acting as a speech event analyzer, a dictation event subsystem, a text event subsystem, and an executor of application programs. The speech signal processor may generate speech event data and send this data to the processor, where it will first be processed by the speech event analyzer. The speech event analyzer may generate a list or set of potential candidates that represent or match the voice input processed by the speech signal processor in the system record. The speech event analyzer may send these candidate sets to the dictation event subsystem. The dictation event subsystem may analyze the candidate set and select the best candidate with the highest degree of similarity. This candidate is then deemed to be the correct translation and the dictation event subsystem communicates this translation to the text event subsystem, which then inputs the translated text into the device.

[0090] Numerous features and advantages of the present disclosure are apparent from this specification, and it is intended by the appended claims to cover all such features and advantages of the present disclosure that fall within the spirit and scope of the present disclosure. Moreover, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the present disclosure to the exact construction and operation as illustrated and described, and therefore, all suitable modifications and equivalents may be resorted to as falling within the present disclosure. [Explanation of symbols]

[0091] 13 Internal body parts 14 Neurostimulators 16 External parts 18 Task Components 200 DBS System

Claims

1. 1. A method for identifying when and / or how to apply deep brain stimulation (DBS) to a patient, comprising: receiving, by a controller, neurophysiological activity data; receiving, by the controller, biometric data relating to the patient; identifying, by the controller, one or more weighting components of the neurophysiological activity data; assigning a weight by the controller to each of the one or more weighting components based on the biometric data regarding the patient; determining, by the controller, whether, when, and / or how to apply the DBS based on a trained algorithm applied to the one or more weighting components; commanding, by the controller, application of the DBS based on the determination; A method comprising:

2. receiving feedback data by the controller subsequent to the DBS; training an algorithm based on the feedback data to determine by the controller whether a level of criteria is met sufficient to stimulate the DBS; commanding, by the controller, application of the DBS based on the determination; The method of claim 1 further comprising:

3. The method of claim 1 , wherein the neurophysiological activity data includes at least one of local field potentials, single and multi-unit neural activity, heart rate, heart rate variability, and / or muscle response.

4. The method of claim 1 , wherein the biometric data about the patient includes at least one of the patient's state of consciousness and / or the patient's activity level.

5. 10. The method of claim 1, wherein determining whether to apply DBS comprises identifying at least one of a benchmark for at least one of a frequency of the received neurophysiological data, a phase of a frequency band of the received neurophysiological data, spikes of individual unit activity, a phase coincidence of multi-unit activity of the received neurophysiological data, and / or a time-locked neural signal.

6. The method of claim 1 further comprising instructing the patient to complete a task.

7. The method of claim 1 , wherein the biometric data is collected when the patient is instructed to complete a task, is in the process of completing a task, and / or has completed a task.

8. The method of claim 6 , wherein the tasks include one or more motor tasks.

9. 9. The method of claim 8, wherein the one or more motor tasks include moving an affected limb.

10. The method of claim 1 , wherein the biometric data is collected while the patient is at rest.

11. The method of claim 1 , wherein the trained algorithm comprises one or more of a convolutional neural network algorithm and a heuristic algorithm.

12. 3. The method of claim 2, wherein the step of commanding application comprises timing the stimulation relative to one or more phases of motor planning and / or relative to one or more phases of motor execution.

13. 10. The method of claim 1, wherein the patient has one or more conditions including ischemic stroke, hemorrhagic stroke, traumatic brain injury, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, or other neurological disorder.

14. 1. A method of treating stroke in a subject, comprising: applying closed-loop deep brain stimulation according to the method of claim 1; A method comprising:

15. 1. A system for identifying when and / or how to apply deep brain stimulation (DBS) to a patient, comprising: a controller configured to receive neurophysiological activity data; Including, the controller is further configured to receive biometric data regarding the patient; the controller is further configured to identify one or more weighting components of the neurophysiological activity data; the controller is further configured to assign a weight to each of the one or more weighting components based on the biometric data related to the patient; the controller is further configured to determine whether to apply the DBS based on a trained algorithm applied to the one or more weighting components; and The controller is further configured to command application of the DBS in response to the trained algorithm. system.

16. the controller is further configured to receive feedback data following the DBS; the controller is further configured to train an algorithm based on the feedback data to determine whether a level of criteria sufficient to stimulate the DBS is met; and the controller is further configured to apply the DBS based on the determination. The system of claim 15 further comprising:

17. 16. The system of claim 15, wherein the neuro-physiological activity data includes at least one of local field potentials, single and multi-unit neural activity, heart rate, heart rate variability, and / or muscle response.

18. The system of claim 15 , wherein the biometric data about the patient includes at least one of the patient's state of consciousness and / or the patient's activity level.

19. 16. The system of claim 15, wherein the controller is further configured to determine whether to apply the DBS, comprising identifying at least one of a benchmark for at least one of a frequency of the received neurophysiological data, a phase of a frequency band of the received neurophysiological data, spikes of individual unit activity, phase congruence of multi-unit activity of the received neurophysiological data, and / or a time-locked neural signal.

20. The system of claim 15 , wherein the controller is further configured to instruct the patient to complete a task.

21. 16. The system of claim 15, wherein the controller is further configured to collect the biometric data when the patient is instructed to complete a task, is in the process of completing a task, or has completed a task.

22. 21. The system of claim 20, wherein the tasks include one or more motor tasks.

23. 23. The system of claim 22, wherein the one or more motor tasks include moving an affected limb.

24. 23. The system of claim 22, wherein the trained algorithm comprises one or more of a convolutional neural network algorithm and a heuristic algorithm.

25. 17. The system of claim 16, wherein the controller is further configured to adjust timing of stimulation relative to one or more phases of exercise planning or relative to one or more phases of exercise execution.

26. 16. The system of claim 15, wherein the patient has one or more conditions including stroke, traumatic brain injury, epilepsy, schizophrenia, obsessive-compulsive disorder, Parkinson's disease, essential tremor, major depressive disorder, or other neurological disorder.

27. The system of claim 15 , wherein the biometric data is collected while the patient is at rest.

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