102 results about "Brain stimulation" patented technology

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

The invention relates to a non-invasive brain stimulation self-adaptive closed-loop regulation and control system. The system comprises a task interaction module which is configured to present a time sensing task to a target object and receive an interaction instruction of the target object; the non-invasive brain stimulation module is used for performing non-invasive brain stimulation; the sensing acquisition module is used for acquiring electroencephalogram data and behavioral data of a target object; the closed-loop regulation and control module comprises at least one block-level regulation and control primary unit, and each block-level regulation and control primary unit comprises a plurality of block-level regulation and control primary units; the trial secondary regulation and control primary unit is used for generating a first feature vector in real time on the basis of the currently collected electroencephalogram data and behavioral data so as to obtain a stimulation parameter of the next trial in the current block; the block-level regulation and control first-level unit is used for processing the currently collected electroencephalogram data and behavioral data to obtain a second feature vector, and based on the second feature vector, adaptive search is carried out under security constraints to obtain stimulation parameters of a next block. By adopting the method, the stability of the stimulation effect can be improved.

The present application relates to a kind of brain stimulation target point fast positioning method based on facial recognition and AI neural navigation, by obtaining the MRI data of user's head, construct the three-dimensional digital brain model including brain function partition information;Acquisition facial three-dimensional point cloud, with the head surface model reconstructed by MRI registration, establish the space mapping of actual head and brain model;Based on this mapping, determine stimulation target point on model.In treatment, real-time tracking head pose using optical system, and in navigation interface fusion display brain model, target point and the real-time position and angle of magnetic stimulation coil.Determine the best target stimulation angle of coil according to the gyri and sulci orientation of target point, guide operator to adjust coil pose by comparing the difference between real-time angle and target angle.The present application realizes the fast, accurate, visual positioning and dynamic maintenance of target point in transcranial magnetic stimulation treatment, and upgrades the traditional experience-dependent operation to individualized, real-time feedback precision navigation treatment.

Embodiments of present disclosure provide systems and methods relating to neuromodulation. In particular, the present disclosure provides systems and methods for administering deep brain stimulation (DBS), and for evaluating the efficacy and efficiency of DBS for the treatment of neurological conditions (e.g., Parkinson's disease).

The present disclosure relates to a method for suggesting personalized and symptom-specific DBS stimulation parameters, comprising the steps of: creating a three-dimensional symptom network library comprising a data-driven three-dimensional model of symptom-specific nerve tracts in a stereotactic space established based on a detailed and comprehensive pathway profile; registering patient data with the three-dimensional symptom network library to analyze how an electrode of a single patient is mapped thereto; stimulation parameters are derived and suggested from baseline symptom severity characteristics for each patient. Another object of the invention relates to a system for predicting DBS parameters, the system comprising an interface for providing DBS parameters to an electrode and receiving signals from a patient, and a computer programmable unit comprising a data memory for storing a network library of three-dimensional symptoms, the three-dimensional symptom network library includes a stored data driven three-dimensional model of symptom-specific nerve tracts in a stereotactic space established based on detailed and comprehensive pathways for predicting DBS parameters.

The invention relates to a transcranial cell stimulation experiment model, which is characterized in that a support structure is used for supporting a skull model, the upper end of the support structure enters the skull model, a placement platform fixed on the support structure is arranged in the skull model, a cell culture dish is placed on the placement platform, two sides of the skull model are respectively provided with a stimulation coil which is obliquely distributed, and the stimulation coils are arranged on the skull model. The power supply unit is used for rapidly discharging to the two stimulation coils so as to induce generation of transient strong pulse current in the stimulation coils, and a 1T-4T strong magnetic field is formed around the stimulation coils. A transcranial cell stimulation experiment method comprises the following steps: firstly, pretreating SH-SY5Y cells, and then putting the pretreated SH-SY5Y cells into a cell culture dish; specific current waveforms are transmitted to the two stimulation coils, and the SH-SY5Y cells are stimulated through a generated electromagnetic field. The method has the beneficial effects that experimental simulation and experimental verification are realized, the understanding of a non-invasive brain stimulation technology is enriched, and a new scientific basis is provided for the treatment of related neurological and mental diseases.

A method includes receiving with a controller, neurophysiology activity data; receiving with the controller, biometric data for the patient; identifying with the controller, one or more weighted components of the neurophysiology activity data; assigning with the controller, based on the biometric data for the patient, a weight to each of the one or more weighted components; determining with the controller, based on a trained algorithm applied to the one or more weighted components, whether to apply the DBS; and instructing application with the controller, based on the determination, the DBS.

Techniques are disclosed for defining a homeostatic window for controlling delivery of electrical stimulation therapy to a patient. In one example, a method includes generating and delivering electrical stimulation therapy to tissue of a patient via electrodes. Further, the method includes adjusting a level of a parameter of the electrical stimulation therapy such that a signal of the patient is not less than a lower bound and not greater than an upper bound. The lower bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce one or more symptoms of a disease while the patient was receiving medication for reduction of the one or more symptoms. Further, the upper bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce the one or more symptoms when the patient was not receiving the medication.

The application relates to a non-invasive brain stimulation adaptive closed-loop regulation system, which comprises a task interaction module configured to present a time perception task to a target object and receive interactive instructions thereof; a non-invasive brain stimulation module for non-invasive brain stimulation; a sensing and collecting module for collecting electroencephalogram data and behavior data of the target object; and a closed-loop regulation module comprising at least one block-level regulation primary unit, each block-level regulation primary unit comprising a plurality of trial-level regulation primary units; the trial-level regulation primary unit is used for generating a first feature vector in real time based on the currently collected electroencephalogram data and behavior data, so as to obtain stimulation parameters of the next trial in the current block; and the block-level regulation primary unit is used for processing the currently collected electroencephalogram data and behavior data to obtain a second feature vector, performing adaptive search under safety constraints based on the second feature vector, and obtaining stimulation parameters of the next block. The method can improve the stability of the stimulation effect.

Disclosed is wearable device for monitoring brain activity and for providing non-invasive stimulation, wearable device comprising: headwear arrangement comprising: electrode arrangement comprising electrodes configured to make electrical contact with skin of user; and flexible cushioning member supporting electrodes; input / output (I / O) arrangement configured to: receive electrical signals (ES) from electrode(s); transmit ES to data processing arrangement (DPA) to process ES for generating brain stimuli (BS); and generate and apply BS generated using a brain stimulation protocol (BSP) to electrode(s), BS applied on skin lying partially in mastoid region; DPA comprising processing unit configured to: receive ES from I / O arrangement; analyse ES with predetermined reference data set, by extracting signal feature(s); apply processing algorithm(s), to map signal feature(s) to stimulation parameter(s); generate BSP comprising stimulation parameter(s); and transmit BSP to I / O arrangement; and power unit(s) supplying electrical power to I / O arrangement and DPA.

This disclosure is directed to devices, systems, and techniques for delivering electrical stimulation. In some examples, a system includes processing circuitry configured to: receive information representative of a bioelectric brain signal recorded from a brain of a patient; and determine, based on the information, at least one pathological frequency of the bioelectric brain signal. Additionally, the processing circuitry is configured to select, based on the at least one pathological frequency, a sequence of pulse bursts at a pulse burst frequency, the sequence of pulse bursts at least partially defining electrical stimulation deliverable to an area of a brain of a patient, wherein adjacent pulse bursts within the sequence comprise different intra-burst pulse frequencies; and control a medical device to deliver the electrical stimulation comprising the sequence of pulse bursts to the area of the brain of the patient.

AI Based Brain Stimulation Device

The invention relates to the technical field of brain stimulation, in particular to a brain stimulation system and method based on optical genetic regulation, and the system comprises a target screening module, an illumination path adjustment module, a neural response extraction module, a regulation trend recognition module and an irradiation parameter linkage module. According to the method, multiple index evaluations are constructed according to the three-dimensional characteristics and discharge frequency information of a neuronal synaptic output path in a target brain region, target neurons with main path peak control capability are screened, anisotropy quantization parameters are calculated according to scattering characteristic differences of tissue structures in an illumination propagation path, and an optical fiber incident angle is adjusted in real time; according to the method, local illumination is utilized to realize directional coverage of specified neurons, the response change trend is identified and regulated by analyzing the maximum amplitude of frequency shift and the number of times of direction reversal, the illumination intensity, pulse width and period parameters are automatically adjusted in combination with response categories, dynamic tuning of the stimulation process is realized, and the precision and the adaptive ability of nerve stimulation are improved.

This invention relates to a method and apparatus for dynamic tracking and follow-up positioning of a robotic arm for brain stimulation. The method includes establishing a spatial mapping relationship between multiple coordinate systems, transforming the coordinates of the stimulation target point and the initial cortical normal vector determined in a three-dimensional digital brain model to the robotic arm's base coordinate system to obtain the first target position and axis, and controlling the robotic arm to align the stimulation coil. Real-time acquisition of head pose change data of the stimulated subject is used to calculate the real-time updated coordinates of the target point in the robotic arm's base coordinate system, thereby determining the second target position and axis that the stimulation coil needs to follow. Combining the geometric and electromagnetic characteristic parameters of the stimulation coil, the tracking target pose in the robotic arm's end-effector coordinate system is calculated. Joint control commands are generated based on the deviation between the target pose and the actual pose to drive the robotic arm movement, thereby stably maintaining the effective stimulation field of the stimulation coil continuously covering the predetermined intracranial target point during head pose changes, solving the problem of off-target movement caused by head motion.

The invention relates to a brain stimulation target quick positioning method based on facial recognition and AI (artificial intelligence) neural navigation, which comprises the following steps of: constructing a three-dimensional digital brain model containing brain function partition information by acquiring head MRI (magnetic resonance imaging) data of a user; acquiring face three-dimensional point cloud, registering the face three-dimensional point cloud with a head surface model reconstructed by MRI, and establishing space mapping between an actual head and a brain model; based on this mapping, a stimulation target is determined on the model. During treatment, the optical system is used for tracking the head posture in real time, and the real-time positions and angles of the brain model, the target spot and the magnetic stimulation coil are fused and displayed on a navigation interface. The optimal target stimulation angle of the coil is calculated according to the cortex sulcus return direction at the target point, and an operator is guided to adjust the position and posture of the coil by comparing the difference between the real-time angle and the target angle. According to the invention, the rapid, accurate and visual positioning and dynamic maintenance of the target spot in transcranial magnetic stimulation treatment are realized, and the traditional operation depending on experience is upgraded to individualized and real-time feedback accurate navigation treatment.

The present disclosure provides systems and methods relating to brain stimulation therapy. In particular, the present disclosure provides systems and methods for identifying effective stimulation patterns for reducing one or more symptoms or a neurological or psychiatric disorder.

A digital method of therapeutic brain stimulation is provided. The method comprises presenting to a user in a UI a group of trials by sequentially displaying symbols against a background. The symbols are randomly displayed one at a time against one several contexts in the background to stimulate the user's parietal or frontal lobes or motor cortex. A user response is received for each display of a symbol indicating whether the symbol is the same or different from an immediately prior symbol for the same context, wherein each trial has a maximum allowed response time for the user input after which any response is counted as a false alarm. A user score is generated for the group of trials, wherein the score compares correct identification and incorrect false alarm responses for the group of trials against a user score of a previous group of trials.

This specification describes techniques for seizure detection in mammals. A first set of electrodes is used to stimulate a region of a brain of a mammal, and an electrical signal sensed by a second subset of electrodes is acquired during stimulation. The electrical signal is segmented into time-segmented portions according to a value of a time parameter. For each time-segmented portion of the electrical signal, a power is determined for the portion of the signal within a frequency band defined by a value of a frequency parameter. A classifier is used to classify the time-segmented portion of the signal as seizure positive or seizure negative based on the determined power within the frequency band. The values of the time and frequency parameters can result in the classifier achieving an effective level of performance defined by an area under a precision-recall curve (AU-PRC) for the classifier of at least 0.5.

The application discloses a battery-free flexible implantable deep brain stimulator, a system and a preparation method, and belongs to the field of implantable biomedical devices. The application comprises: the piezoelectric device is used for receiving a pulse ultrasonic signal with a fundamental frequency of 1MHz emitted by an extracorporeal ultrasonic generator, and generating an alternating current signal consistent with the stimulation frequency and stimulation duration of the pulse ultrasonic signal through a piezoelectric effect; the rectifier circuit is used for converting the alternating current signal into a direct current signal; the stimulation electrode is used for acting the direct current signal on a target brain area; and the piezoelectric device is composed of a piezoelectric array of a plurality of square Sm-doped PMN-PT piezoelectric single crystal bulk materials with a center frequency of 1MHz. The application adopts the square Sm-doped PMN-PT piezoelectric single crystal bulk material with a center frequency of 1MHz to form the piezoelectric array, and due to the adoption of the Sm-doped PMN-PT array with a specific structure, the maximum output power can be obtained in the resonant state of the resonant frequency of 1MHz, so that wireless, battery-free and safe real-time deep brain stimulation is realized.

Provided herein are wire fixation instruments and methods of using wire fixation instruments to mount a stimulation wire to a skull. The lead fixation instrument may be used to mount a stimulation lead for deep brain stimulation. The wire fixation instrument may maintain the relative position of the stimulation wire while allowing the wire to axially float. The wire fixation instrument may include one or more flexure or bellows couplers to maintain the position of the stimulation wire while isolating forces acting on the wire.

Devices, systems, software, and methods are provided for administering adaptive deep brain stimulation (DBS) to a subject who has been treated with medication and / or continuous DBS in order to relieve residual motor symptoms. In particular, DBS is performed with a neural recording device that records brain electrical signals from neural activity associated with a residual motor symptom and automatically adjusts deep brain stimulator settings and / or delivers electrical stimulation to the brain when pre-specified patterns of neural activity associated with the residual motor symptom are detected. Machine learning or linear computational models are used to detect and classify patterns of neural activity associated with the residual motor symptom.

[Problem] To provide a pseudo sensation provision device and the like. [Solution] A pseudo sensation provision device for providing a pseudo sensation to a subject comprises: a brain stimulation unit for stimulating the brain of the subject; and a stimulation information control unit for obtaining stimulation-unit control information for controlling the brain stimulation unit. The stimulation information control unit: has a pseudo sensation model that is a trained model constructed by using, as training data, stimulation information to the brain of a subject organism and sensation information of the subject organism; and outputs output stimulation information when sensation information for providing a pseudo sensation to the subject has been inputted to the pseudo sensation model. The stimulation information control unit obtains the stimulation-unit control information by using the output stimulation information.

In some variations provided herein, the system for deep brain stimulation includes an implantable device that acquires and stores recordings of neural activity signals and applies electrical stimulation. The system also includes a personal controller device that establishes a first wireless connection to the implantable device. The personal controller device transmits power to the implantable device, and the implantable device transmits recordings of neural activity signals to the personal controller device via the first wireless connection. The system further includes a clinician programmer device that establishes a second wireless connection based on activation of the first wireless connection to receive recordings of neural activity signals from the implantable device. The clinician programmer device sets one or more stimulation parameters based on the neural activity signal recordings.

Embodiments described herein include a method for electrically stimulating target tissue of a brain using a first array of electrodes and a second array of electrodes different from the first array of electrodes. The first array of electrodes may include a first primary electrode and one or more secondary electrodes, and the second array of electrodes may include a second primary electrode and one or more secondary electrodes. The method may include causing, during a charging phase, current to flow between the first primary electrode and the second primary electrode, each of the first array of electrodes and the second array of electrodes disposed to one side of a target tissue of the brain such that the current passes through the target tissue of the brain. The method may include causing, during a discharging phase, a majority of a discharge current to pass through non-target tissue of the brain.

The present disclosure relates to the treatment of patients with mid- to late-stage Parkinson's Disease using subthalamic nucleus deep brain stimulation (STN-DBS) to a target a defined region of the brain. In particular, by positioning and / or programming the DBS electrode to stimulate cortical input fibers from the primary motor and supplementary motor areas (but not the pre-SMA), improved therapeutic benefits are obtained.

This specification describes techniques for seizure detection in mammals. A first set of electrodes is used to stimulate a region of a brain of a mammal, and an electrical signal sensed by a second subset of electrodes is acquired during stimulation. The electrical signal is segmented into time-segmented portions according to a value of a time parameter. For each time-segmented portion of the electrical signal, a power is determined for the portion of the signal within a frequency band defined by a value of a frequency parameter. A classifier is used to classify the time-segmented portion of the signal as seizure positive or seizure negative based on the determined power within the frequency band. The values of the time and frequency parameters can result in the classifier achieving an effective level of performance defined by an area under a precision-recall curve (AU-PRC) for the classifier of at least 0.5.

Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinson's Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include a novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

In general, devices, systems, and techniques are described for managing therapy based on monitoring sensed signals from a patient. In one example, a digital health system includes processing circuitry configured to receive sensed data indicative of bioelectric brain signals sensed during delivery of brain stimulation therapy to a patient, determine aggregate characteristic values of the bioelectric brain signals for a repeatable duration during the period of time, generate a graph comprising the aggregate characteristic values for the repeatable duration; and control a user interface to display the graph.