129 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.

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 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.

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 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.

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 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 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.

The application discloses a brain-computer interface-based attention-enhanced cognitive closed-loop rehabilitation intervention method, which comprises the following steps: firstly, collecting electroencephalogram signals and related behavior information synchronously and completing time alignment processing; secondly, pre-processing and extracting features of the collected signals; on the basis, constructing a multi-dimensional brain state fusion model to realize comprehensive evaluation of attention and cognitive participation state; further generating control instructions through dynamic threshold determination and a gated decision mechanism, and adaptively adjusting cognitive task parameters and intervention intensity according to the control instructions; and updating system parameters through a feedback mechanism, so as to form a closed-loop intervention process. The application solves the problems of single attention representation, unstable state determination and lack of adaptive closed-loop regulation in the existing brain-computer interface cognitive intervention method, and realizes an attention-enhanced cognitive closed-loop intervention mechanism based on multi-dimensional brain state fusion.

The application discloses an immersive space virtual-real interaction method and relates to the technical field of virtual reality; after the game starts, the first sub-time zone of each game stage is taken as a starting data collection window, EEG signal data of a player is collected, an alpha wave trend graph is constructed, through a series of calculation and analysis, including determining a reference frequency range, identifying an abnormal point, calculating an alpha wave variation ratio, an alpha wave evaluation value of the player in the starting data collection window is obtained, this process can accurately evaluate the brain state of the player in the starting stage of the game, the change trend of the player's immersion is predicted in advance and the intensity of the tactile feedback is adjusted in time, the player's frequent "out of the game" in the game process is avoided, the coherence and immersion of the game experience are maintained, when the alpha wave evaluation value of the player shows that the immersion may decrease, the tactile feedback is enhanced to attract attention, the game participation is improved, and the player can continuously immerse in the game.

To provide methods of correlating a brain signal to intentional and unintentional changes in a brain state.SOLUTION: Disclosed herein are methods of analysis to extract and assess brain data collected from subject animals, including humans, to detect intentional and unintentional brain activity and other unexpected signals. These signals are correlated with higher cognitive brain functions or unintended, potentially adverse events, such as a stroke or seizure, and with translation of those signals into defined trigger events or tasks.SELECTED DRAWING: Figure 6

The application discloses an ECT anesthesia electroencephalogram denoising method based on double-domain interactive attention and mask pre-training. The method extracts the electroencephalogram signal after anesthesia induction and before electric stimulation release in the electric shock treatment process, constructs the time domain representation and the frequency domain representation after short-time Fourier transform, models the local time sequence information and the global frequency spectrum information by using the double-domain feature extraction network, and introduces the cross-domain interactive attention mechanism to enhance the reservation ability of effective electroencephalogram components and the suppression ability of artifact noise. Then, the high-fidelity denoised electroencephalogram signal is recovered through the decoding reconstruction module, and the generalization performance of the model in the real clinical scene is improved by combining the supervised pre-training and the mask self-supervised domain adaptation strategy. The method can be used for automatic denoising processing of ECT anesthesia electroencephalogram, and provides a reliable signal basis for subsequent efficacy prediction, brain state evaluation and clinical auxiliary decision-making.

The present invention discloses a method and apparatus for constructing a brain-computer interface system for neurofeedback training, relating to the field of neurofeedback training. The method comprises: designing a T-shaped channel layout for a near-infrared brain imaging device, including: employing a T-shaped arrangement of paired light sources and receivers on a plane, wherein adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels thus formed can be mapped to the entire brain; configuring a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device. The present invention can determine the regulatory effects of resting-state brain states during various task feedback training sessions.

The invention discloses an immersive space virtual-real interaction method, and relates to the technical field of virtual reality. According to the method, after a game starts, a first sub-time zone of each game stage serves as an initial data collection window, EEG signal data of a player is collected, an alpha wave trend chart is constructed, and an alpha wave evaluation value of the player in the initial data collection window is obtained through a series of calculation and analysis including determination of a reference frequency range, identification of abnormal points and calculation of an alpha wave transformation ratio. The process can accurately evaluate the brain state of the player in the initial stage of the game, pre-judge the change trend of the immersion of the player in advance and adjust the tactile feedback intensity in time, so that the player is prevented from frequently playing in the game process, the continuity and immersion degree of game experience are maintained, and when the alpha wave evaluation value of the player shows that the immersion is possibly reduced, the game experience is improved. Tactile feedback is enhanced to attract attention, the game participation degree is improved, and players are continuously immersed in the game.

The invention discloses a deep learning-based brain anesthesia state inversion evaluation method based on an EEG data model. Firstly, a neuron group model suitable for an anesthesia state is constructed, and the neuron group model is based on a Jansen-Rit model, covers multiple neuron groups and has a specific kinetic equation. Then determining a value range of model parameters, sampling to generate a combination, and generating a simulated EEG signal to construct a training set; designing a deep learning model for training, and learning a parameter mapping relation; in practical application, a tested EEG signal is collected and preprocessed and then is input into a trained model inversion parameter, and then the anesthesia depth index ADI is calculated according to the parameter so as to evaluate the anesthesia brain state. The method has high accuracy in evaluation of anesthesia depth, and provides an effective means for anesthesia monitoring.

Provided are systems, methods, and devices for providing mediation of a traumatic brain injury. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with a traumatic brain injury. 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 traumatic brain injury. 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 application belongs to the technical field of electric digital data processing, and particularly relates to an adaptive ensemble learning model optimization method for electroencephalogram signal classification, which comprises the following steps: acquiring a multi-channel electroencephalogram sample segment, extracting features containing time-frequency energy, phase locking and spatial mode, the phase locking containing phase locking values among the multi-channels, and constructing three base learners; constructing a weighted brain network through the phase locking values and constructing a brain state feature vector; taking the fusion weight of each base learner as an optimization variable, combining the weight into a particle, grouping according to feature preferences, constructing fitness, iteratively updating the particle based on the fitness, and iteratively optimizing to obtain an optimal weight; weighting and fusing the prediction probabilities of each base learner by using the optimal weight, selecting the category corresponding to the maximum prediction probability as the classification result, and triggering re-optimization based on the change of the adjacent brain state feature vector. The method realizes online adaptive optimization of the weight, and improves the accuracy and long-time stability of motor imagery electroencephalogram signal classification.

The application relates to the technical field of medical informatics and intelligent treatment control, and discloses an electroencephalogram synchronous electroacupuncture stimulation calibration method and system based on embedded AI. The embedded AI module is used for collecting and intelligently analyzing brain electrical signals of a patient in real time, generating brain state characteristics, dynamically adjusting electroacupuncture stimulation parameters in combination with subjective feedback of the patient, and forming a closed-loop feedback system. The system carries out individualized baseline calibration before treatment, improves the accuracy of brain state recognition, continuously monitors the brain electrical signals during treatment, dynamically updates the brain state characteristics, adjusts the electroacupuncture parameters according to the brain state characteristics, and ensures the individualization and dynamic optimization of the treatment. The system also has long-term learning ability, continuously accumulates treatment experience, optimizes the association rules, and improves the accuracy and efficiency of the treatment. The application significantly improves the pertinence, response speed and stimulation calibration accuracy of electroacupuncture treatment, and provides a new solution for individualized medical treatment.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.