95 results about "Brain state" patented technology

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

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 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 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.

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.

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.

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.

The invention belongs to the technical field of biomedical signal processing and nerve regulation and control, and discloses a closed-loop time interference nerve regulation and control system and method based on synchronous electroencephalogram signals. The closed-loop nerve regulation and control system integrates multi-channel electroencephalogram collection, neural state real-time recognition, brain network function connection analysis, traceability positioning calculation and individualized time interference stimulation strategies, an upper computer module can obtain electroencephalogram signals and analyze brain function states in real time, abnormal network structures are automatically recognized, and the brain function states are analyzed in real time. A double-target stimulation path is formulated by combining a nerve source positioning result, so that the defects of inaccurate positioning, incapability of responding to brain state change in real time and unpredictable stimulation effect of a traditional nerve stimulation method are overcome; the closed-loop mechanism of'perception-decision-execution-evaluation 'of the system not only enhances the effectiveness of stimulation, but also remarkably improves the safety, controllability and individualization level of the regulation and control process.

A brainstate estimate of a subject's brain is generated, including by detecting a peak shape in an EEG signal, measuring features from detected peaks, and classifying the EEG signal based at least in part on the locations of points in a multidimensional space. This includes obtaining a plurality of totally disjoint regions that the multidimensional space is divided up into and a plurality of probabilities associated with the plurality of totally disjoint regions. A combined value is determined based at least in part on the locations of the plurality of labeled points within the multidimensional space. The EEG signal is classified based at least in part on the combined value. The brainstate estimate is generated based at least in part on the classification of the EEG signal.

This invention discloses a brain-controlled wheelchair method, system, computer device, and storage medium based on multimodal bioelectrical signal fusion, relating to the field of human-computer interaction technology. The method includes: synchronously acquiring bioelectrical signals and environmental perception data and aligning their transmission; calculating attention entropy and brain-muscle coupling degree to generate an active control intention index, and constructing multimodal fusion features by combining reference signals synthesized from a generation model; decoding the user's intention distribution and constructing an environmental semantic map, and associating them to form a joint feature tensor; extracting brain state parameters to adjust the stiffness coefficient of the virtual clamp, and combining it with signal difference correction coefficients; inputting the joint feature tensor and the corrected stiffness coefficients into a decision module, generating motion control parameters through constraint optimization and outputting them; driving the wheelchair to perform actions, and using feedback data for model updates. This invention achieves a safe, adaptive, and long-term stable brain-controlled wheelchair through dual verification of attention entropy and brain-muscle coupling degree, spatiotemporal collaborative decision-making of intention and environment, and meta-learning lifelong adaptation.

A method includes generating a trading performance model for a trading activity involving a set of decisions by a set of expert traders. The trading performance model includes a set of input data sets, a set of data processing workflows operating on the input data sets, and a set of trading decision outputs resulting from interaction of the expert traders with a user interface representing the trading performance model. The method includes generating a brain state model representing a sequential set of brain states of the set of expert traders that characterize brain states measured during the interactions of the expert traders with the user interface representing the trading performance model, assessing the quality of the trading decisions, determining a preferred pattern of trader brain state sequences, and modifying a subsequent trading activity.

A closed-loop system for asynchronous brain control of at least one task includes a brain state decoder configured to decode neural signals of a user into control signals for controlling the at least one task, a task interface module configured to transmit the control signals to the at least one task, store state information including a series of messages about each of the at least one task, and select one message of the series of messages about the at least one task for transmission to the user, and a brain state encoder configured to map the one message received from the task interface module into a brain state composite image for transmission to the user.

The invention discloses a method and device for sensing brain state staging fluctuation, and relates to the technical field of robots and artificial intelligence synthesis.The method comprises the steps that human body electroencephalogram signals and behavioral data when a to-be-tested person executes a task are obtained; inputting the human body electroencephalogram signals into a brain state short-term fluctuation perception model to obtain brain load changes of the to-be-tested person; inputting the behavioral data into a brain state long-term fluctuation perception model to obtain skill level change of the to-be-tested person; and inputting the brain load change and the skill level change into the brain state short-term and long-term fluctuation comprehensive information fusion model, and generating a comprehensive output result of the to-be-tested person. According to the invention, the brain-computer adaptive capability can be improved.

The invention relates to a closed-loop tRNS system and method based on energy landscape analysis and brain state decoding, and the method comprises the steps: building an individualized model through offline calibration of an offline modeling unit, and generating a brain state decoding information table through energy landscape analysis; performing real-time electroencephalogram monitoring by using a signal acquisition module; then the online decoding and stimulation decision-making unit carries out real-time state decoding, and then the online decoding and stimulation decision-making unit triggers and applies tRNS; and finally, periodically evaluating the effect and adaptively optimizing parameters. According to the method, dynamic space-time precise regulation and control are achieved, static space positioning (right side apical leaf upward return) and a dynamic time window (specific brain state) are combined, a normal form of'fixed target and fixed parameters' of a traditional nerve regulation and control technology is broken through, and the regulation and control space-time precision is improved to a millisecond level and a network level.

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.

Systems and methods for implementing in-ear brain-computer interfaces are described. For example, a method may include accessing measurements of electrical potential of a first electrode, a second electrode, and a third electrode that are in contact with an inside surface of an ear canal; determining a reference signal based on measurements of electrical potential of the first electrode; determining a ground signal based on measurements of electrical potential of the second electrode; determining a first electroencephalography signal based on measurements of electrical potential of the third electrode and based on the reference signal; and estimating a brain state based on the first electroencephalography signal.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

The application discloses a brain state staging fluctuation perception method and device, relates to the technical field of robots and artificial intelligence, and comprises the following steps: acquiring human brain electrical signals and behavior data of a person to be measured when the person to be measured performs a task; inputting the human brain electrical signals into a brain state short-term fluctuation perception model to obtain brain load changes of the person to be measured; inputting the behavior data into a brain state long-term fluctuation perception model to obtain skill level changes of the person to be measured; and inputting the brain load changes and the skill level changes into a brain state short-term and long-term fluctuation comprehensive information fusion model to generate a comprehensive output result of the person to be measured. The application can improve brain-computer mutual adaptation capability.

This invention relates to the field of artificial intelligence-assisted decision-making technology, providing a method and system for multimodal neural modulation individualized parameter-assisted decision-making. The method includes acquiring multimodal data of the subject, calculating tolerance, load, and activation demand indices, and determining the subject's initial set of stimulation parameters; generating a subset of candidate targets based on a target brain network driven by EEG signals and a preset scoring function; acquiring the EEG response signals of each candidate target after applying the initial stimulation parameters, calculating the scoring index of the EEG response signals to obtain the response score of each candidate target, and determining the final target; based on the initial stimulation parameters, determining whether the subject's current brain state is within a preset plasticity window, and then combining this with safety constraints to determine the stimulation duration for the final target, generating individualized stimulation parameters, and providing doctors with personalized and accurate auxiliary decision-making suggestions.