29 results about "Electrical brain stimulation" patented technology

The patent application describes an adaptable neuromodulation device for transcranial electrical brain stimulation. The system can generate complex poly-modulated waveforms by combining multiple pulse parameters in a randomized manner across dimensions like amplitude, frequency, phase, timing, and polarity. It leverages both digital and analog modulation techniques for waveform versatility. The device features pathway optimization algorithms that tune stimulation using neural feedback data to target specific brain region shapes with greater precision. It also enables closed-loop operation for responsiveness to physiological changes. The application details the mathematical and computational foundations, spanning graph theory to generate digital twins. It highlights applications from cognitive enhancement to seizure control. Overall, the innovation promises versatility and precision in non-invasive neuromodulation through adaptable, optimized waveforms grounded in sophisticated modeling.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

A method is provided for precision dosing of electrical stimulation of the brain. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of an electro-stimulation device including a plurality of electrodes positioned on a head of a subject. The method also includes obtaining measurements that indicate a tissue type at each voxel inside the head of the subject based on an imaging device. The method also includes determining, with a processor, a value of one or more parameters of the electro-stimulation device based on the tissue type measurements at each voxel such that the electro-stimulation device is configured to generate a value of one or more parameters of an electric field at each voxel inside the head of the subject to improve the treatment outcome of the subject.

The present disclosure relates to a biomarker for providing information on brain electrical stimulation, and a method for providing information for brain doping diagnosis (bD2) using the same. According to the present disclosure, there is provided a method for determining, using a urine sample, whether a subject has received brain electrical stimulation, which may be used as a means for establishing regulations and countermeasures for abuse of brain doping of which safety has not yet been confirmed.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

A method and apparatus are provided for determining stimulation parameters for DBS. The method includes receiving first data indicating a value of a local field potential (LFP) over a first time period from first contacts positioned adjacent a brain region. The method further includes receiving third data indicating a value of a LFP over second time periods from the first contacts, where the brain region is stimulated by second contacts over second time periods based on stimulation parameter values. The method further includes determining a frequency band encompassing a difference between a first frequency spectrum of the first data and a second frequency spectrum of the third data. The method further includes determining a value of the second frequency spectrum over the frequency band for each stimulation parameter value. The method further includes determining a value of an optimal stimulation parameter based on the value of the second frequency spectrum and each stimulation parameter value.

The present application relates to a brain-computer interface device, electrodes, and an EEG signal processing method, device, computer equipment, storage medium, and product, including multiple electrodes and a control device. The multiple electrodes are used to acquire EEG signals of a target object and output stimulation signals to the target object; the control device includes a data acquisition module and a signal generation module; the data acquisition module is electrically connected to each electrode through an interface, and the signal generation module is electrically connected to each electrode through an interface; the control device is used to control the data acquisition module to acquire EEG signals through multiple electrodes, and simultaneously control the signal generation module to output stimulation signals through multiple electrodes. The brain-computer interface device of the present application controls the data acquisition module and the signal generation module simultaneously through the control device, so that the data acquisition module can acquire EEG signals through multiple electrodes, and the signal generation module can output stimulation signals through multiple electrodes, thereby achieving the simultaneous acquisition of EEG signals and output of EEG stimulation signals.

The present invention relates to the field of brain-computer interface technology, and discloses a brain-computer interaction paradigm, implementation method, brain-computer interaction method and system. The brain-computer interaction paradigm presents an electroencephalogram (EEG) stimulation interface to the user's stimulation attention eye, and simultaneously presents an environmental video to the user's video attention eye; the EEG stimulation interface and the environmental video are switched in distance based on the user's active and passive attention selection mechanism. The brain-computer interaction method has an observation mode and a control mode, and switches between the observation mode and the control mode based on the user's eye movements. In the observation mode, the first-person perspective video and the third-person perspective video are synchronously tiled from left to right on the main canvas; in the control mode, the user's EEG features are acquired in real time. When the EEG feature is judged to be a control-state EEG feature, the corresponding EEG stimulation category is determined and the control instruction corresponding to the EEG stimulation category is output. The present invention can limit user misoperation to the greatest extent and realize high-performance immersive brain-computer interaction in complex scenarios.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

The disclosure relates to a brain stimulation system (102), comprising: a) a computer program product, e.g. a data carrier, preferably a non-transitory data carrier, or a data stream, comprising program code (P1, P1a) which, when loaded and executed on an electronic control unit (108), provides an operation control system, orb) an electronic control unit (108) with a program code (P1, P1a) loaded on the electronic control unit (108), wherein the program code (P1, P1a), when executed on the electronic control unit (108), provides an operation control system (200), wherein the operation control system (200) is configured to control a brain training and / or stimulation session for a brain, and wherein the operation control system (200) comprises a plurality of control system functionalities, the control system functionalities comprising: —a stimulation functionality (202) which is configured to cause the electronic control unit (108) to issue a stimulation command to an electrical brain stimulation device (122) to cause the electrical brain stimulation device (122) to perform an electrical brain stimulation procedure; and—a presentation functionality (204) which is configured to cause the electronic control unit (108) to issue a presentation command in order to present a task to be performed by a user (120) on a user interface (110), wherein preferably the stimulation functionality (202) and the presentation functionality (204) are linked (254 to 262) or linkable to one another.

The wearable electroencephalogram monitoring device comprises an electroencephalogram cap, an earphone and an electroencephalogram collecting device, a plurality of electroencephalogram electrodes are arranged on the electroencephalogram cap, and the electroencephalogram collecting device is installed on the electroencephalogram cap and comprises a shell. A power supply interface, a power supply circuit, a sound source interface, an audio generation circuit, an audio drive circuit, an earphone interface, a signal acquisition interface, a signal acquisition circuit, a control circuit, a storage circuit, an operation indication circuit and a communication circuit are integrated in the shell; the electroencephalogram acquisition assembly and the sound stimulation assembly are integrated to form the electroencephalogram acquisition device, the electroencephalogram stimulation device is installed on the electroencephalogram cap, generation of sound stimulation and acquisition of signals are facilitated, occupied space is small, the electroencephalogram acquisition device is neat and orderly, circuits in the electroencephalogram acquisition device are optimally designed, and the electroencephalogram acquisition device is convenient to use. And multi-channel electroencephalogram signals can be collected more accurately.

Disclosed herein are deep-brain stimulation (DBS) systems and methods for the treatment of Tourette Syndrome (TS). The DBS methods described herein comprise a stimulation paradigm that may help to reduce the frequency and / or severity of motor and vocal tics, as well as rapid and involuntary muscle motions that are characteristic of TS. One variation of a method for the treatment of TS comprises adjusting electrical stimulation to a target brain region based on changes or variations in the neural activity signals in the target brain region. One example of a method described herein comprises monitoring the power values (e.g., spectral power values) of one or more frequency bands of the acquired neural activity signals and adjusting electrical stimulation parameters based on at least one variation or change of the monitored power values.

The application belongs to the technical field of brain emotion regulation, and particularly relates to an audio-visual environment emotion personalized evaluation method and device based on range fusion decision, which comprises collecting and preprocessing brain emotion information by taking visual and auditory single sensory channel, visual multiple factor synergy and audio-visual cross-sensory channel as electroencephalogram stimulation; a range fusion decision model is established and trained, the model is used to obtain the influence degree of different audio-visual environment factors on different emotions, and the environment is adjusted according to the evaluation result to realize personalized emotion regulation. The application designs combinations of different audio-visual environment factors, obtains related electroencephalogram data, calculates the weight of different audio-visual environment factors of each experimental object, evaluates the influence of different audio-visual environments on emotions, and according to different emotion regulation requirements, corresponding audio-visual environment design is carried out, and finally, personalized self-adaptive adjustment of the environment is realized, which can better reflect real life situations and has higher feasibility and applicability.

The invention discloses a brain stimulation device and method using plant electromagnetic waves, and relates to the technical field of electroencephalogram regulation and control, and the device comprises a donor module for accommodating a living plant, and a collection module for collecting the plant electromagnetic waves outwards radiated by the living plant; collected plant electromagnetic waves are subjected to energy level improvement and direction focusing through an acceleration focusing module to form gathered electromagnetic waves, and the gathered electromagnetic waves are directionally transmitted to a brain stimulation terminal through a conduction module with a wave guide cavity and a transmission channel so as to act on electromagnetic wave stimulation signals of a craniocerebral area of a user body; the control feedback module detects brain waves of a user in real time through the brain wave monitoring unit, and the central processing unit adjusts output parameters of the acceleration focusing module and the stimulation terminal to realize closed-loop regulation and control of gathered electromagnetic waves; therefore, engineering collection of plant electromagnetic waves and output of controllable electroencephalogram stimulation signals are achieved, and the problems that an existing electroencephalogram regulation and control mode is rigid in stimulation and difficult to match with an electroencephalogram natural rhythm are solved.

The invention provides a charge balance optimization device and method for an electroencephalogram stimulation closed-loop system. The method comprises the steps that in the off-line calibration stage, contact impedance of a testee is simulated, a series of control signals with different stimulation parameters are applied, the inherent charge unbalance amount under different parameters is measured, and a charge unbalance compensation rule is established; the method comprises the following steps: in an on-line measurement stage, outputting positive and negative bidirectional stimulation current signals according to preset stimulation parameters for actual testee contact impedance, calculating real-time charge unbalance caused by positive stimulation current and negative stimulation current, and analyzing total charge unbalance in combination with a charge unbalance compensation rule; in the on-line compensation stage, the stimulation current amplitude of the actual testee in the next period is dynamically adjusted based on the total charge unbalance amount. According to the invention, the detection accuracy of the charge unbalance in the brain electrical stimulation system is improved, a foundation is laid for subsequent charge balance, the charge balance precision is improved, and the damage to the tested tissue is further reduced.

The invention relates to an electroencephalogram high-order connection analysis method based on a time-spectrum super network, which is technically characterized by comprising the following steps of: constructing an electroencephalogram stimulation experiment group, collecting multichannel electroencephalogram data in a stimulation process, preprocessing multichannel electroencephalogram signals, constructing a brain time-spectrum super network, performing feature extraction and quantization on the brain time-spectrum super network, and analyzing the electroencephalogram high-order connection of the brain. Time domain connection strength features and spectral domain graph topological features are obtained and analyzed, a classification model is constructed to distinguish subjects with effective and ineffective spinal cord stimulation, and correlation between the features and clinical behavior scores is revealed through statistical analysis. According to the method, a multi-channel electroencephalogram signal acquisition and graph signal processing method is combined, and the brain network response process in a real application scene can be simulated, so that the objectivity and the interpretability of neural regulation effect evaluation are improved, the quantitative characterization function of brain time-spectrum collaborative dynamics under the neural regulation condition is realized, and the neural regulation effect evaluation accuracy is improved. And a new technical means is provided for quantitative analysis of complex brain function state changes.

This invention discloses a method, apparatus, device, and readable storage medium for brain stimulation. The brain stimulation method includes: setting up a video group containing multiple videos with different frame rates but the same duration; collecting brain signals generated when a user views the videos in the video group; and inputting the brain signals into a pre-trained brain-matching model to obtain the frame rate of the video viewed by the user. Based on the brain stimulation method provided by this invention, compared with traditional brain stimulation methods, the user experience is more comfortable and less prone to visual fatigue. It avoids the pitfalls of traditional stimulation methods, preventing changes in light or graphics in a short period that could cause brain nerve contraction and abnormal discharge, potentially inducing epileptic seizures. Correspondingly, it expands the applicable population of the brain stimulation device to a certain extent and improves the adaptability of brain stimulation. Based on a neural network model trained with a large amount of data, the analysis of brain signals is more accurate and the error is smaller.

The present invention relates to the technical field of brain-computer interfaces, and discloses a multi-interface fusion dynamic brain-computer interaction method and terminal based on frequency-phase information multiplexing. Based on the user's offline electroencephalogram (EEG) experiment, the maximum number U of EEG stimulations for the user to achieve the optimal EEG stimulation classification effect, the frequency-phase information of the U-classified EEG stimulations, and the first correspondence between the U-classified EEG stimulations and the EEG signal characteristics are obtained. The interactive interface after multi-interface fusion is dynamically partitioned to obtain Z sub-regions and the brain control instructions included in each sub-region. The frequency-phase information of the U-classified EEG stimulations is assigned to each brain control instruction. Based on the EEG signal characteristics induced by the flashing rule of the EEG stimulation corresponding to a certain brain control instruction when the user gazes at it, the EEG stimulation classification is obtained based on the first correspondence. The sub-region is determined based on the user's line-of-sight landing coordinates. Combining the EEG stimulation classification and the determined sub-region, a brain control instruction is output. The present invention can improve the efficiency of brain-computer interaction operations.

The patent application describes an adaptable neuromodulation device for transcranial electrical brain stimulation. The system can generate complex poly-modulated waveforms by combining multiple pulse parameters in a randomized manner across dimensions like amplitude, frequency, phase, timing, and polarity. It leverages both digital and analog modulation techniques for waveform versatility. The device features pathway optimization algorithms that tune stimulation using neural feedback data to target specific brain region shapes with greater precision. It also enables closed-loop operation for responsiveness to physiological changes. The application details the mathematical and computational foundations, spanning graph theory to generate digital twins. It highlights applications from cognitive enhancement to seizure control. Overall, the innovation promises versatility and precision in non-invasive neuromodulation through adaptable, optimized waveforms grounded in sophisticated modeling.

Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features or sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features or sleep stages to target sleep dysfunction. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

The present invention discloses a real-time evaluation method for transcranial electrical stimulation effects, which relates to the technical field of brain electrical stimulation effects. By integrating MRI modeling, HD-tDCS electrode optimization, electric field simulation, and genetic algorithm parameter configuration, and combining multi-modal neural response acquisition, multi-task brain state prediction based on the attention mechanism, and Granger causal path modeling, the present invention constructs a real-time evaluation mechanism for the entire "stimulation → nerve → behavior" chain. Compared with the existing evaluation methods that only rely on electroencephalogram changes or single behavioral indicators, the present invention can simultaneously quantify the spatial targeting of stimulation, neural activation specificity, behavior improvement degree, and causal connectivity strength, and drive parameter self-feedback optimization through comprehensive scoring, significantly improving the real-time performance, accuracy, and individual regulation ability of transcranial electrical stimulation effect evaluation.

In general, devices, systems, and techniques are described for automating the selection of parameters used to define adaptive stimulation therapy. In one example, a system includes processing circuitry configured to control sensing circuitry to sense a plurality of bioelectric signals from a brain of a patient via a plurality of different electrode combinations, receive information representative of the plurality of bioelectric signals, and determine spectral power information from the information representative of the plurality of bioelectric signals. The processing circuitry may also select, based on the spectral power information, a frequency for monitoring, via one electrode combination of the plurality of electrode combinations, subsequently sensed bioelectric signals associated with deep brain electrical stimulation, and determine, based on the spectral power information at the selected frequency, one or more threshold values that define adjustment to at least one parameter value that defines the deep brain electrical stimulation.

Examples of the presently disclosed technology provide new systems and methods for performing direct targeting for DBS procedures (and other related procedures) using a single pre-operative scan of a patient's brain. Examples can achieve this single-scan direct targeting by leveraging mesh vertex-based correspondences between patient-specific 3D mesh brain structure representations across prospective and historical DBS procedures. Examples can leverage such correspondences to accurately predict DBS lead placement coordinates for a prospective DBS procedure based on historical DBS lead placement coordinates from the historical DBS procedures. Accordingly, a clinician performing the prospective DBS procedure can utilize the predicted DBS lead placement coordinates for surgical planning purposes.

Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features or sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features or sleep stages to target sleep dysfunction. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

The EEG-based brain electrical stimulation measurement and control circuit, the measurement and control all-in-one machine and the measurement and control method in the embodiment of the invention comprise a multi-channel EEG pickup circuit, a multi-channel constant current source circuit and a signal switching circuit, the multi-channel EEG pick-up circuit and the multi-channel constant current source circuit are connected with a plurality of electrode links working in a parallel mode through the signal switching circuit; the signal switching circuit realizes parallel connection with a plurality of electrode links through a built-in multi-channel analog switch matrix; when the input level value received by the multi-channel analog switch matrix is a first level, the electrode link is conducted with the multi-channel EEG pickup circuit; and when the input level value received by the multi-channel analog switch matrix is a second level, the electrode link is conducted with the multi-channel constant current source circuit, so that integrated operation and management of EEG data acquisition and electroencephalogram stimulation application of a subject can be realized, the working efficiency is effectively improved, and the system is suitable for the technical field of medical care.

The EEG-based electroencephalography (EEG) monitoring and control circuit and integrated monitoring and control device in this embodiment include: a multi-channel EEG pickup circuit, a multi-channel constant current source circuit, and a signal switching circuit; the multi-channel EEG pickup circuit and the multi-channel constant current source circuit are both connected to multiple electrode links operating in parallel through the signal switching circuit; the signal switching circuit achieves parallel connection with multiple electrode links through a built-in multi-channel analog switch matrix; when the input level received by the multi-channel analog switch matrix is ​​a first level, the electrode link is connected to the multi-channel EEG pickup circuit; when the input level received by the multi-channel analog switch matrix is ​​a second level, the electrode link is connected to the multi-channel constant current source circuit. This enables integrated operation and management of subject EEG data acquisition and EEG stimulation application, effectively improving work efficiency and is applicable to the field of medical and nursing technology.

The invention discloses a multi-target electric field intensity intelligent optimization method and system based on individualized closed-loop nerve regulation, and relates to the field of nerve regulation and brain electrical stimulation. The method comprises the following steps: constructing an electric field guide field matrix based on a finite element model, and sequentially injecting unit current under the condition of a fixed backflow electrode to establish a linear relation between the current and the electric field; according to the electric field intensity definition of the target area, constructing an optimized target function, constraining the target area to reach the set electric field intensity, and minimizing the electric field energy of a non-target area; processing non-convex constraint by adopting a convex-concave planning method, and introducing a slack variable and a penalty term to improve convergence; and setting a sparsity constraint to control the maximum number of activated electrodes, and finally outputting an optimized electrode flow injection scheme meeting intensity control and safety limitation. The method supports multi-target and multi-area combined control, has good stimulation focusing performance and practical feasibility, and is suitable for individualized transcranial electrical stimulation treatment and closed-loop nerve regulation and control systems.

The invention relates to a brain-computer intelligent collaboration system and method based on a neural feedback state machine, belongs to the technical field of man-machine intelligent collaboration, and solves the problems that an existing brain-computer intelligent collaboration system lacks a closed-loop feedback link and is poor in adaptive capacity. The system comprises an artificial intelligence module used for receiving and processing an image set to obtain an artificial intelligence discrimination image set and a difficult target image set; the man-machine intelligent module is used for applying an electroencephalogram stimulation signal obtained based on the difficult target image set to the user, obtaining a brain cognitive activity signal generated by the user, and responding to a feedback regulation instruction to carry out nerve regulation on the user; and the neural feedback state machine is used for obtaining a collaborative decision result and a feedback regulation and control instruction based on the brain cognitive activity signal and the artificial intelligence discrimination image set, adjusting parameters of the artificial intelligence module based on the feedback regulation and control instruction, and performing neural regulation and control on the user through the man-machine intelligent module.

The disclosure relates to a brain stimulation system, comprising: a) a computer program product, comprising program code which provides an operation control system, or b) an electronic control unit with a program code, wherein the program code provides an operation control system, wherein the operation control system is configured to control a brain training and / or stimulation session for a brain, and comprises a plurality of control system functionalities, the control system functionalities comprising: a stimulation functionality which is configured to cause the electronic control unit to issue a stimulation command to an electrical brain stimulation device to cause the device to perform a procedure; and a presentation functionality which is configured to cause the electronic control unit to issue a presentation command in order to present a task to be performed by a user on a user interface, wherein preferably the stimulation functionality and the presentation functionality are linked or linkable.