151 results about "Brain state" patented technology

The delivery of neurostimulation to a subject using a closed-loop neurostimulation device (e.g., using a brain stimulation device to provide neurostimulation to a subject's brain) is controlled based on a global optimization-based temporal prediction framework. As a result, the brain stimulation device (e.g., a transcranial magnetic stimulation (“TMS”) device) is synchronized with the ongoing neural state (e.g., brain state) in real time. For instance, a brain recording is analyzed to extract the brain process of interest (e.g., frequency of brain oscillations) and used train a prediction algorithm. After that, a stimulation stage is implemented, in which the individual brain state is analyzed in real-time, the occurrence of the biomarkers (e.g., brain oscillation phase) is predicted, and the stimulation is triggered at the expected time.

The invention discloses a real-time evaluation method for a transcranial electrical stimulation effect, and relates to the technical field of brain electrical stimulation effects, and the real-time evaluation method comprises the following steps: fusing MRI modeling, HD-tDCS electrode optimization, electric field simulation and genetic algorithm parameter configuration, combining multi-modal nerve response collection, multi-task brain state prediction based on an attention mechanism, and Granger causal path modeling. And a real-time evaluation mechanism oriented to a full chain of'stimulation-nerve-behavior 'is constructed. Compared with an existing evaluation mode which only depends on electroencephalogram change or a single behavior index, the method has the advantages that the space targeting, the nerve activation specificity, the behavior improvement degree and the causal connection strength of stimulation can be quantified at the same time, and parameter self-feedback optimization is driven through comprehensive scores; and the instantaneity, the accuracy and the individualized regulation and control capability of transcranial electrical stimulation effect evaluation are remarkably improved.

The invention provides a nervous system closed-loop regulation and control system based on transcranial opto-acoustic cooperation. The nervous system closed-loop regulation and control system is composed of EEG monitoring equipment, personal information data, an opto-acoustic cooperation system, a multi-mode large model and a psychological disease database. Aiming at a transcranial infrared light and ultrasonic co-stimulation technology of a patient, through optimization of stimulation parameters, photoacoustic bidirectional regulation and control are realized, a multi-modal large model is used as a base, multi-modal data fusion and reinforcement learning are used as technical means, closed-loop photoacoustic co-stimulation system equipment is designed, and intervention parameters are automatically optimized; an effective regulation and control method for psychological diseases is realized through a non-intrusive brain stimulation technology, and adaptive adjustment and closed-loop feedback of parameters are realized. Brain waves of a patient are detected in real time, personal information of the patient is collected, the information is integrated and transmitted into the system after evaluation, intervention is adjusted in real time according to different brain states, and feedback is utilized to adjust parameters of next intervention and stimulate the precise regulation and control process.

Methods and devices for changing a brain state of a person from an initial brain state to a target brain state are described. The method comprises the following steps: receiving information representing the initial brain state, wherein the information comprises the structural composition and the functional architecture of the brain; estimating a likelihood of the brain changing from the initial brain state to the target brain state based at least in part on the received information; determining a non-invasive brain stimulation scheme based at least in part on the received information and the estimated likelihood of the brain changing from the initial brain state to the target brain state; and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation scheme. The method also includes using the brain information to provide information for a design to compute a generic artificial intelligence agent.

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.

The invention provides a brain organism closed-loop rehabilitation system and a control method thereof, belongs to the field of brain-computer interfaces and rehabilitation medical treatment, and is used for solving the problems of insufficient brain state perception, poor adaptability and low robustness of a brain organism rehabilitation system in related technologies. The method comprises the following steps: synchronously acquiring multi-modal physiological signals by cooperating with physiological signal monitoring, task induction and regulation and control equipment modules, removing artifacts through preprocessing and redundancy check, extracting neuroplasticity characteristics, dynamically adjusting weight decoding brain activity intentions, regulating and controlling stimulation parameters in a closed loop, and training an optimization model in combination with a multi-center federation; accurate and highly-adaptive rehabilitation regulation and control are realized, and the rehabilitation effect and the model generalization are improved.

The invention discloses a hidden multi-mode brain state real-time monitoring and closed-loop intervention system, and relates to the technical field of intelligent wearable equipment. The system comprises a cap body, and further comprises a multi-point electrode array used for collecting EEG (electroencephalogram) signals, EOG (eye movement) signals and EMG (electromyography) signals of a human body; the brain blood oxygen monitoring module is used for monitoring brain blood oxygen saturation; the signal receiving and processing module is used for preprocessing and analyzing data acquired by the multi-point electrode array and the brain blood oxygen monitoring module; the multi-mode stimulation module is used for implementing sensory and nerve stimulation on the user according to a judgment result of the state judgment module so as to intervene in a drowsiness or attention decline state; and the power supply module and the communication unit are used for providing a working power supply for the modules needing power supply in the system and carrying out data communication with external terminal equipment. The system can monitor the waking degree of the user with high precision and carry out closed-loop feedback intervention by various stimulation means.

The invention discloses a transcranial alternating current stimulation system and a transcranial alternating current stimulation method based on event-related potential double-component alignment. The system comprises an individualized feature extraction module, a dynamic phase mapping module, a real-time synchronous triggering module and a closed-loop feedback module, wherein the individualized feature extraction module extracts an ERP component latency period and a dominant oscillation frequency based on task state electroencephalogram data; the dynamic phase mapping module establishes phase alignment of an ERP component time window and a tACS waveform to realize a brain oscillation control process; the real-time synchronous triggering module calculates an evoked potential incubation period and a visual presentation incubation period of a corresponding task item, accurately matches a peak value of the evoked potential with a corresponding peak value of a tACS waveform, reads a current phase of the tACS waveform in advance, performs prospective prediction, adjusts the starting time of each stimulation, and realizes millisecond-level stimulation synchronization; the closed-loop feedback module monitors the brain state on line and dynamically corrects alignment parameters to ensure that the stimulation effect is stable; according to the method, accurate phase alignment of tACS and brain endogenous ERP components is realized, neural activities in a specific cognitive process can be effectively enhanced or inhibited, and the accuracy and individualization level of neural regulation and control are improved.

The invention provides a brain-computer interface closed-loop nerve regulation and control method, a storage medium, a system and a program product, and the method comprises the following steps: circularly executing the following steps according to a first period until an end condition is met: controlling transcranial magnetic stimulation equipment to run for a first time length according to a set stimulation parameter to stimulate a testee; collecting an electroencephalogram signal sequence of the testee in the target brain area; the electroencephalogram signal sequence is processed, a preset index is obtained, and the preset index is used for reflecting the active condition of the target brain area; and processing the preset index by using a deep reinforcement learning network to obtain a stimulation parameter of the next period. According to the method, the brain state is adjusted according to accurate individual characteristics in a self-adaptive closed-loop mode, and a more accurate and effective nerve regulation and control scheme can be provided.

The invention discloses an electroencephalogram transformation simulation system and method for different brain states based on a Hopv model, and the electroencephalogram transformation simulation system constructs a virtual brain model by using an objective nonlinear dynamic model, carries out the quantitative description of the brain states, and achieves the accurate simulation of the transformation between different brain states and the prediction of a target spot. According to the method, the weighted phase lag index and the similarity of the network attributes of the weighted phase lag index are used as a parameter optimization target when the virtual brain signal is constructed, the electroencephalogram simulation level of the virtual signal is improved, and the target spot in the brain state transition process can be recognized and predicted by extracting the co-activation brain area of the co-activation mode and combining virtual stimulation. According to the method, a quantitative basis is provided for virtual brain model construction and brain state regulation and control, and an individualized nerve regulation and control strategy can be formulated.

Devices, systems, software, and methods are provided for treating chronic pain in an individual using deep brain stimulation (DBS). An implantable brain stimulation and neural recording device is used to identify personalized pain biomarkers to automate brain stimulation using adaptive DBS algorithms in response to pain-associated brain states. Methods are also provided for identifying effective personalized brain stimulation sites for an individual through a systematic, multi-site brain mapping trial and tailoring real-time adaptive DBS programs to an individual to provide significant pain relief.

The invention discloses an electroencephalogram classification method and system based on physiological prior guidance and cross-scale information fusion, electronic equipment and a storage medium, and the electroencephalogram classification method comprises the steps: obtaining sound stimulation data, and carrying out the matching processing of the sound stimulation data to enable the sound stimulation data to have consistency in physical characteristics; the collected electroencephalogram data are preprocessed; dividing the electroencephalogram data into a test set and a training set, and determining hyper-parameters of model training; feature extraction is carried out on the training set electroencephalogram data through a CNN and Transform parallel structure; and the extracted features are classified through a classifier module, and a classifier is trained and verified. According to the method, the global feature extraction capability of Transform and the local feature capture capability of CNN are fused, through a multi-level physiological prior feature guide method, brain state data of different levels are extracted and fused as a classification feature set, and the classification accuracy is effectively improved.

The invention relates to a closed-loop vagus nerve intervention control system combined with meditation treatment, and the system comprises a physiological signal collection module which is used for collecting an EEG (electroencephalogram) signal and an ECG (electrocardiosignal) signal of a patient in real time; the data processing and analyzing unit is used for calculating heart-brain and brain-heart bidirectional coupling coefficients on the basis of the acquired signal data and evaluating the execution control function indexes of the patient in real time; the stimulation parameter regulation and control module is used for dynamically adjusting the frequency, the pulse width and the current intensity of the transaural vagus nerve stimulation taVNS according to the calculated bidirectional coupling coefficient; the meditation guidance module is used for providing personalized meditation guidance based on the real-time heart and brain state; the feedback control module is connected with the stimulation parameter regulation and control module and the meditation guidance module and is used for realizing collaborative optimization of stimulation parameters and meditation intervention through a closed-loop adaptive mechanism; and the execution control evaluation module is connected with the feedback control module and is used for improving the risk decision-making capability of the system for joint meditation treatment by setting a plurality of task evaluation modes.

The invention provides a brain function dynamic prediction device and equipment based on rTMS and a medium, and relates to the technical field of brain data processing.The device is used for obtaining multi-mode magnetic resonance imaging data and conducting preprocessing to generate a BOLD time sequence; carrying out Hilbert transform on the BOLD time sequence, and determining a phase coherence matrix of different brain regions at the same time point; performing weighted eigenvector dynamic analysis on the phase coherence matrix to generate a weighted eigenvector; performing clustering analysis on the weighted feature vectors to generate functional connection brain state clustering data; determining the similarity between the function connection brain state clustering data and the Yeo resting state network, and generating a function connection brain network state; state change data between functional connection brain network states is determined, and a state transition probability matrix is generated; and generating an rTMS prediction result according to the transition probability matrix and the brain index. The prediction efficiency can be improved.

The invention relates to the technical field of transcranial electrical stimulation, and discloses a human brain transcranial stimulation circuit and a human brain transcranial stimulation method, and the method comprises the steps: obtaining the brain state data of a to-be-stimulated user; according to the brain state data, analyzing a connection relationship between brain regions, and constructing a brain network map; based on the relationship between the node degree value and the medium centrality value of each node in the brain network atlas and the corresponding preset value, analyzing an abnormal region in the brain network atlas in combination with a preset anomaly detection model to obtain a brain target region; generating a stimulation strategy according to the brain target area; optimizing the stimulation strategy in combination with the brain state data to realize closed-loop control of the stimulation strategy; by combining the real-time state of the user, dynamically generating the stimulation signal, accurately positioning the brain target area and optimizing the stimulation strategy, the brain target area can be stimulated more effectively, the treatment process is more scientific and reasonable, and the treatment effect and efficiency are improved.

A method for biosignal data transformation can include: determining a set of biosignal data, determining a brain state representation (e.g., an embedding) based on the biosignal data, and determining a biomarker value based on the brain state representation. The method can optionally include training a model (e.g., training a model used to determine the brain state representation), and / or any other suitable steps. A system for biosignal data transformation can include a biosignal device and a computing system.

The invention belongs to the technical field of brain dynamics modeling, particularly relates to a regulation and control scheme auxiliary analysis method based on a disease specificity whole brain dynamics model, and aims to realize accurate screening of nerve regulation and control schemes. Comprising the steps that S1, multi-mode magnetic resonance imaging data are obtained and preprocessed, structural connection, a brain region BOLD time sequence, static function connection and dynamic function connection are obtained, and prior constraint values of myelination and cortical atrophy are obtained through calculation based on the preprocessed data; s2, parameterizing the local return coupling strength of the model based on a prior constraint value, coupling the models of different brain regions through the structural connection obtained in the step S1, and constructing a whole brain dynamic model through parameter optimization; s3, training a variational auto-encoder by using different groups of static function connections and dynamic function connections; and S4, applying different regulation and control schemes on the whole brain dynamic model, obtaining a brain state evolution trajectory induced by regulation and control through the trained variational auto-encoder, and screening an effective regulation and control scheme.

Methods, systems, and devices are described for reconstructing spatially resolved electrical activity in the brain. In some example embodiments, EEG and MRI data are used to estimate volumetric distribution of electrostatic potential inside the MRI domain throughout the entire brain. Spatially and temporally varying field estimates can be generated using a brain wave model which is based on weakly evanescent transverse cortical wave propagation and constrained using the tissue properties gained from the MRI data. The disclosed techniques enable brain activity imaging with high spatial and temporal resolution, thereby providing a tool for assessing functional brain states and monitoring changes in those states in relation to various normal and pathological conditions.

A system which can be used for carrying out a method for determining an efficacy of a transcranial magnetic stimulation (TMS) treatment, including:delivering TMS to a patient using a TMS protocol in a TMS treatment session;recording EEG signals before and after the delivering during the treatment session;recording EEG signals after completion of the treatment session;analyzing the recorded EEG signals to generate values of one or more EEG parameters;determining an acute effect and an intermediate effect of the TMS protocol on a brain state of the patient based on a change in values of the one or more EEG parameters;determining an efficacy of the TMS protocol based on the determined acute effect and the intermediate effect.

The invention discloses a signal fusion processing method for a motor imagery training brain-computer interface, and particularly relates to the technical field of neural signal processing. Multi-channel scalp electroencephalogram data of a user are collected in real time, a time-varying space covariance matrix sequence is constructed through Riemannian geometric space mapping analysis, and a phase amplitude coupling index sequence between different brain rhythms is analyzed and calculated through cross-band coupling dynamics; performing feature hierarchy joint coding on the spatial covariance matrix sequence and the phase amplitude coupling index sequence to generate a real-time neural representation tensor; calculating the distribution divergence of the real-time neural representation tensor relative to the static reference feature manifold in the initial training stage so as to quantitatively represent the mismatch degree; and finally, dynamically adjusting a weighting coefficient and a fusion structure of the multi-modal signal fusion device according to the characterization mismatching degree, and outputting a motion control instruction matched with the current brain state of the user in real time. According to the method, the motion intention decoding precision and stability in the motion imagination training process are improved.

The invention relates to the technical field of cloud native storage, and particularly provides a high-availability service split-brain state processing method, system and device and a storage medium, and the method comprises the steps: monitoring the states of a main node and a standby node through a third-party node, and appointing an arbitration main node name based on a monitoring result; the main node and the standby node mutually monitor the states of opposite ends; when the main node or the standby node confirms that the state of the opposite end is abnormal, the main node or the standby node obtains the name of the arbitration main node from a third-party node, and whether the main node or the standby node provides main node service is confirmed by comparing the consistency of the name of the arbitration main node and the name of the main node or the standby node; a split-brain detection rule is configured in the switching auxiliary scripts of the main node and the standby node, so that the main node and the standby node execute split-brain detection before switching the main state and the standby state, and the split-brain detection rule comprises obtaining the opposite-end state. According to the method, the split-brain state of the high-availability service is effectively solved, the stability of the high-availability service is improved, and data loss is avoided.

The invention relates to a personalized sleep-aiding regulation and control method and system fusing multi-band characteristics and closed-loop feedback, and relates to the technical field of sleep regulation and control. According to the method, a cross-band coupling matrix is constructed, a nerve-physiological synergy index is calculated, environment-brain state interaction potential energy is calculated based on different band powers and environment sound pressure levels, and the nerve-physiological synergy index and the environment-brain state interaction potential energy are combined into a coupling state vector; solving the coupling state vector to judge the stability of the sleep state; on the basis, personalized sleep induction dynamic attractor intensity is calculated, and different sleep regulation and control strategies are generated according to the steady state / unsteady state and the corresponding attractor intensity. According to the invention, the unique sleep dynamics of the user can be understood and self-adaptively regulated.

The invention relates to the technical field of brain-computer interfaces, and discloses an electroencephalogram signal space-time correlation feature analysis method and device based on a graph neural network. The method comprises the steps that a target electroencephalogram data set is collected, and the brain state category of electroencephalogram data corresponding to the target electroencephalogram data set is obtained through a hierarchical density clustering algorithm; extracting a signal segment of the corresponding electroencephalogram data in the target time period and mapping the signal segment into a two-dimensional grayscale image; extracting symbolic elements and corresponding levels from the two-dimensional grayscale image; constructing a multi-scale hierarchical model according to the signal segments, and extracting multi-scale features reflecting space-time relevance from the symbolic elements; calculating an overlapping region of the two-dimensional grayscale image; constructing a mode set of a preset target theme by taking the symbolic element as a mode and taking the overlapping region information as mode content; and training a preset optimization model by using the mode set to extract visual space-time correlation characteristics in combination with the two-dimensional grayscale image so as to complete space-time correlation characteristic analysis of the electroencephalogram signal data.

The invention relates to a user state detection method and device, an earphone and a storage medium. And acquiring PPG photoplethysmography data of the user, wherein the PPG data is acquired by a PPG sensor carried on an earphone worn by the user. And then, analyzing the PPG data to obtain the physiological indexes of the user. Then, according to the physiological indexes of the user, the brain state of the user is determined; and finally, generating feedback information according to the brain state of the user, and sending the feedback information to the earphone so as to carry out voice reminding on the user. Therefore, the convenience of detecting the user state and the accuracy of determining the brain state are improved.

A system operates by: sending gamified neurofeedback displays for display via a graphical user interface of a client device and receiving client device interactions with the graphical user interface from the client device; receiving neurosensing device signals via at least one neurosensing device corresponding to a user of the client device; preprocessing and filtering the neurosensing device signals to reduce artifacts and to produce filtered signals corresponding to a plurality of different brain waves of the user; generating frequency and time analysis data based on the filtered signals; extracting feature data based on the frequency and time analysis data; generating, via an artificial intelligence (AI) neuro-classification engine trained via machine learning, neuro-classification data based on the feature data, generating brain assessment data based on the neuro-classification data, generating, via at least one gaming application, the gamified neurofeedback displays based on client device interactions and / or generating neurofeedback results based on the neuro-classification data.

Provided are systems, methods, and devices for providing mediation or alleviation of sleep disorders and insomnia. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with sleeping disorders and insomnia. A first processing device is configured to generate multiple brain state parameters characterizing one or more features of a brain state of the user. A second processing device is configured to generate models of the brain of the user based on the plurality of brain state parameters and the plurality of measurements, and determine, using the models and training data comprising one or more mediation data points, a mediation procedure for reducing one or more symptoms of the sleeping disorder or insomnia. The mediation procedure is provided to one or more entities, and one or more control signals are generated by the controller based on the mediation procedure.

The invention relates to a minimally invasive implantable frontal lobe brain-computer interface method and device, and the method comprises the steps: constructing an individualized brain three-dimensional model according to CT and MRI image data of a patient, presetting an optimal nasal implantation path, and carrying out the implantation of the patient through a nose under the dual guidance of a nasal endoscope and a three-dimensional navigation system, constructing a skull base passage through minimally invasive trimming of a nasal cavity / paranasal sinus structure, and performing real-time matching and overlapping on an endoscope view and the three-dimensional model by using a rigid body transformation algorithm to display the position of a surgical instrument; windowing at a preset target point, implanting the flexible electrode array into a target brain region according to a planned depth and angle through a controllable propulsion mechanism, and establishing a neural signal acquisition or stimulation interface; intelligent mode recognition is carried out on the collected neural signals, the intention or brain state of the user is decoded, and corresponding neural stimulation or feedback signals are generated. According to the method, accurate, safe and minimally invasive clinical intervention is realized through a full-chain technical scheme of individualized planning, real-time navigation, minimally invasive implantation and biological integration, and the application value of a brain-computer interface technology is remarkably improved.

Systems and methods to allow for generating a simulated humanoid. The simulated humanoid operates in a simulation space with simulated humanoid having a whole brain emulation module. The whole brain emulation module includes a virtual stimuli input module that is configured to receive or capture stimuli input data. The whole brain emulation module includes an encoder that is configured to translate the stimuli input data into a simulated functional neurodata frame. The whole brain emulation module also includes a brain state module that maintains a current brain state corresponding to a current functional neurodata frame of the simulated humanoid.

Disclosed herein is a brain stimulation method and system for a mammal. The system includes an electroencephalogram (EEG) headset (110) having a plurality of sensors for detecting electrical signals of a patient; and an EEG processing device (120) for determining a stimulation scheme based on the electrical signal. The EEG processing device (120) comprises: a brain state inferring module (124) for determining an inferred brain state of the patient; and a learning module (126) for learning a stimulation scheme based on the detected electrical signal and the inferred brain state. The system also includes a neural regulator (130) for delivering stimulation to the patient based on the stimulation regimen.

Provided herein are methods of neurofeedback. In some aspects, the methods of neurofeedback are individualized for a subject based on the neural activity of the subject. In some aspects, the individualized neurofeedback methods involve providing feedback to the subject regarding the subject's own neural activity in order to facilitate the subject volitionally controlling their own neural activity, such as to promote or inhibit a cognitive state.