56 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 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 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.

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 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 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.

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.

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.

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.

This invention relates to the field of biometric recognition technology, specifically to a method and apparatus for classifying and recognizing brain states based on dynamic graph convolution. The method includes acquiring fMRI data of the brain over a time period; acquiring a functional connectivity matrix; acquiring a graph convolution model; and using the graph convolution model to obtain the corresponding brain functional state by applying the functional connectivity matrix of the brain to be identified. This invention effectively utilizes temporal information to model and classify the brain's state at different points in time.

The invention provides a brain state regulation and control method and system, a medium and electronic equipment. The brain state regulation and control method comprises the following steps: acquiring an electroencephalogram signal of a target subject; according to the electroencephalogram signal, based on a linear time-invariant system theory, constructing a subject state equation of the target subject; according to the tested state equation, based on the Pontryagin minimum principle, obtaining optimal control input and a target control track corresponding to the optimal control input; determining a key control node of the target subject; generating target regulation and control parameters of the target subject according to the optimal control input, the target control track and the key control node; the target regulation and control parameter is used for carrying out brain state regulation and control on the target subject. According to the method, the nerve regulation effect can be improved.

The application provides a neural feedback training system, method and device based on an electroencephalogram large model. The system comprises an electroencephalogram acquisition module, a preprocessing module, a model training module, a data analysis module and a visualization module. The electroencephalogram acquisition module is used to acquire a first electroencephalogram data set and reference electroencephalogram data of a plurality of target groups. The preprocessing module is used to preprocess the first electroencephalogram data set and the reference electroencephalogram data. The model training module is used to train a reference base model to obtain a target base model. The target electroencephalogram model is determined according to a neural feedback training target and the target base model. The data analysis module is used to decode a brain state of the target electroencephalogram data to obtain a brain state feature vector. The visualization module is used to generate a visualization result according to the brain state feature vector. The embodiment of the application can improve the effectiveness and universality of neural feedback training.

This invention provides a head-mounted intelligent wearable neuromodulation and sleep state regulation device and method based on neural coupling tuning and state transfer induction, including a flexible contact array, a state recognition module, a state transfer induction module, a neural coupling modulation module, a stimulation output module, and a feedback optimization module. The system acquires brain state feature vectors and, based on sleep stages and state regulation goals, divides the head into multiple neural functional node regions. Based on the brain state feature vectors, it dynamically generates node combination relationships, node activation sequences, and node collaborative paths. The state transfer induction module controls multimodal stimulation signals to form rhythmic synchronization, phase coupling, and node collaborative regulation, and outputs the signals to the probes corresponding to the target nodes. Simultaneously, it collects feedback signals in real time and dynamically optimizes the topological relationships between nodes and stimulation parameters to achieve closed-loop transfer regulation of brain states between different sleep stages. This invention can improve the individual adaptability, regulation accuracy, and safety of sleep state regulation.

The application discloses a self-adaptive closed-loop neuromodulation decision method and device and electronic equipment, and the method comprises the following steps: obtaining a corresponding target feedback variable based on an electrophysiological signal of a target object at a current sampling moment, wherein the target feedback variable comprises at least one electrophysiological signal feature; obtaining a dynamic target value at the current sampling moment according to the target feedback variable based on a pre-constructed dynamic control target model; and obtaining a target stimulation parameter corresponding to the current sampling moment based on the target feedback variable and the dynamic target value. The method can effectively improve the accuracy of neuromodulation by constructing a dynamic control target model to obtain a real-time dynamic target value, and then making a closed-loop neuromodulation decision to obtain a corresponding target stimulation parameter, so that real-time and personalized self-adaptive closed-loop neuromodulation decision can be realized when the application is used for neuromodulation under a dynamic brain state.

A method for aiding the detection of drug effect using anaesthesia-induced patterns in an electroencephalogram (EEG), comprising: obtaining EEG data corresponding to one or more EEG signals of a patient under general anaesthesia measured using an EEG, wherein the one or more EEG signals are time-varying and comprise a time series of segments comprising at least a repeating first segment type and a repeating second segment type, wherein instances of segments corresponding to the first segment type alternate with instances of segments corresponding to the second segment type; and determining a signal metric comprising a series of time ordered metric datapoints, wherein each metric data point is determined based on: a comparison between at least two segments temporally associated with the metric point; and / or a comparison between at least two different measures of at least one segment temporally associated with the metric point, wherein the signal metric is a measurement of an effect of an anaesthetic drug regimen administered to the patient, and related methods and systems.

This invention belongs to the fields of medical image processing, neural engineering, and artificial intelligence-assisted diagnosis. It provides a Parkinson's disease identification system, medium, and product based on brain network control energy characteristics. Based on the time series of brain region functional activities, it extracts brain states at rest and calculates the temporal dynamic characteristics of each brain state. Using the whole-brain structural connectivity matrix as the network topology, it constructs a linear network control model to calculate the optimal control energy characteristics for brain state transitions and the energy regulation capacity characteristics of whole-brain regions. Based on brain dynamics feature sets, brain network energy feature sets, and brain network graph theory feature sets, it extracts a feature set significantly related to Parkinson's disease and constructs a multi-dimensional fusion feature matrix. Using this multi-dimensional fusion feature matrix as input, it trains the Parkinson's disease identification model. Based on the multimodal brain imaging data of the subject and the trained Parkinson's disease identification model, it outputs the Parkinson's disease identification result. This achieves non-invasive, accurate, and early identification of Parkinson's disease.

The application relates to the technical field of brain image and electroencephalogram data processing, in particular to a brain abnormality data processing method and device, wherein the method comprises the following steps: acquiring neural image and electro-physiological data of a target user; performing brain network segmentation on the neural image to obtain sub-brain networks corresponding to different brain function areas; extracting a plurality of biomarkers based on structure and function dimensions from the neural image and the electro-physiological data; performing specific sub-brain network selection according to the biomarkers by using the sub-brain networks corresponding to the different brain function areas to obtain a first brain network graph; and evaluating the brain state of the target user according to the matching condition of the first brain network graph and a second brain network graph. The multimodal data processing method provided by the application has the advantages of multimodal brain data, can evaluate the brain network condition of the user from different dimensions and modal fusion, has individualized brain network state evaluation capability, and has good universality and realizability.

The invention discloses a cerebral apoplexy monitoring method and system based on cerebral oxygen metabolism rate imaging, and relates to the technical field of medical care. A cerebral apoplexy monitoring system based on cerebral oxygen metabolism rate imaging comprises a data acquisition and calculation module, a data preprocessing module, a data fusion and imaging module, a brain state prediction module and a comprehensive evaluation and planning module. According to the method, the current brain oxygen metabolism rate of the brain tissue is obtained through formula calculation based on the preprocessed blood oxygen parameter data and the relative blood flow index data, the oxygen supply and oxygen consumption conditions of the brain tissue can be reflected at the same time, the brain oxygen supply and demand state can be comprehensively reflected, more comprehensive evaluation is provided for the brain oxygen metabolism state, and the brain oxygen metabolism rate is improved. And the evaluation accuracy of the cerebral apoplexy monitoring method and system based on cerebral oxygen metabolism rate imaging is improved.

This invention discloses a method and system for guiding sleep disorder treatment using fNIRS, relating to the field of medical technology. The method includes: intelligently sensing a user's pre-sleep state using multimodal sensing devices and initiating full-bandwidth signal acquisition; parallel processing of fNIRS and EEG signals to extract cerebral blood oxygenation characteristics and sleep stage information in real time; generating a quantified, real-time treatment objective function based on the multimodal state through dynamic arbitration; calling a user-specific Gaussian process model and optimization algorithm to calculate and output personalized recommended treatment parameters; driving an execution unit to implement intervention, and collecting post-intervention signals for effect evaluation and model updating, forming a closed-loop adaptive control system of "perception-decision-intervention-learning". This invention solves the problem of existing technologies' difficulty in achieving real-time brain state decoding and precise intervention during natural sleep, improving the personalization and real-time adaptability of treatment.

The invention discloses a programmable array probe for monitoring a craniocerebral state and a monitoring system. The programmable array probe comprises a flexible front-end signal acquisition module and a rear-end signal processing module connected with the flexible front-end signal acquisition module, the flexible front-end signal acquisition module comprises a top encapsulation layer, a middle sensing layer and a bottom structure layer which are sequentially arranged from top to bottom, and a wet adhesion hydrogel interface layer covering the surface of the top encapsulation layer; the bottom structure layer is connected with a plurality of column electrodes which penetrate through the middle sensing layer and are arranged in parallel; the middle sensing layer comprises one or more flexible sensor array layers which are arranged in a laminated mode, the flexible sensor array layers are selected from an electroencephalogram electrode sensor array layer and a brain tissue oxygen monitoring sensor array layer, and different flexible sensor array layers are electrically connected with the bottom structure layer through different column electrodes; the programmable array probe provides a reliable and efficient execution scheme for monitoring the craniocerebral state in the operation, the detection accuracy and the adhesion effect are improved, and the abnormal state of the craniocerebral environment of a patient is fed back in time.

This invention discloses an implantable closed-loop neuromodulation system and method, relating to the field of neuroengineering, aiming to solve the problems of poor electrode adaptability and slow closed-loop response in existing implantable neuromodulation systems. The system includes intracranial electrodes, a one-to-one matching adapter plate for adapting different electrodes, a feedthrough component, and an implantable host fixed to the skull surface. The processing unit within the host decodes neural signals in real time within 10 ms to determine the brain state; if an abnormality is detected, the stimulation output unit selects a pattern from a preset safe stimulation pattern library to generate stimulation commands for intervention. This application achieves multi-electrode adaptation through the adapter plate and millisecond-level closed-loop response through high-speed processing, improving the timeliness and accuracy of intervention and enhancing safety.

The invention relates to the technical field of brain-computer signal recognition, and discloses a brain state recognition method and device for a dynamic brain network based on electroencephalogram phase amplitude coupling, and the method comprises the steps: obtaining a multi-channel electroencephalogram signal, and constructing the dynamic brain network based on the electroencephalogram phase amplitude coupling; the method comprises the following steps: performing community analysis on a brain network through a community detection method, determining a consensus node community structure of each time slice, comparing the similarity of the network community structures among the time slices, and analyzing a cross-subject and cross-time stable community state of the dynamic brain network as a reference state by adopting consensus clustering and K-means clustering algorithms. And performing similarity fitting on the brain network of the original time slice and the reference state, and extracting a space-time dynamic index of a brain network community state to complete brain state recognition. Compared with the prior art, the brain state change can be accurately recognized, and more effective technical support is provided for the application fields of neuroscience research, brain-computer interfaces, cognitive function evaluation and the like.