1272 results about "Brain region" patented technology

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

The invention provides a brain parenchyma MRI image-based infarction change prediction method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a brain parenchyma MRI image, constructing a multi-modal image set, carrying out the screening according to a screening standard, carrying out the artifact recognition, calculating a quality score, carrying out the preprocessing, obtaining standard image data, carrying out the feature extraction, and carrying out the prediction of the infarction change of the brain parenchyma MRI image. Obtaining a brain region segmentation mask; performing feature extraction to obtain multi-modal feature data, constructing a topological relation among different brain regions, extracting high-order semantic features, obtaining brain region feature data, and performing selection to obtain high-quality feature data; constructing a time sequence characteristic matrix, distributing weights, generating residual output, performing prediction through a full-connection neural network, generating an infarction change prediction result, generating a prediction prognosis result, and generating a comprehensive prediction report.

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

The invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention provides a closed-loop nerve regulation method and device, a medium and a program product, and relates to the technical field of nerve regulation. The method comprises the steps that an electroencephalogram collection module collects electroencephalogram signals in real time and provides real-time feedback data for nerve regulation and control; the magnetic resonance imaging module provides high-precision brain structure and function positioning information; the three different stimulation modules (transcranial alternating current stimulation, low-field magnetic field stimulation and transcranial magnetic stimulation) can stimulate brain regions with different depths and ranges; the multi-mode navigation module carries out accurate positioning based on the magnetic resonance image; the electroencephalogram detection module analyzes the collected signals; and the central control module coordinates the work of each module. According to the multi-module cooperative working mode, accurate stimulation of different areas and different depths of the brain is achieved, meanwhile, the stimulation effect is ensured through real-time monitoring, and therefore the accuracy and effectiveness of nerve regulation and control are improved.

The invention relates to the technical field of brain function imaging, in particular to a target crowd recognition method and device based on neural blood flow feature multi-level fusion, and the method comprises the steps: collecting brain nerve blood flow signals of normal and target crowds by using synchronous EEG-fNIRS equipment, preprocessing and extracting single-mode features, and obtaining a single-mode feature set; a multi-frequency local nerve and blood vessel coupling analysis method is used for calculating nerve and blood vessel coupling strength characteristics, a space co-positioning method is used for unifying nerve and blood flow modal space information, the position precision is improved, and data layer fusion is achieved; and integrating and analyzing different modal feature space relationships by using a Yeo7 brain template to realize feature layer fusion, and finally training various machine learning classifiers by using significant difference features to realize identification of a target population. Therefore, the problems that in the prior art, macroscopic signal correlation is paid more attention to, so that analysis of the synergistic effect of cross-modal space information in a specific brain region is deficient, and deep understanding of a neurovascular function mechanism and brain region role revelation are restricted are solved.

The invention discloses a neurodegenerative disease diagnosis method for constructing a dynamic brain network based on adaptive graph structure learning, and belongs to the technical field of artificial intelligence. The invention provides a self-adaptive graph structure learning multi-level nerve disease diagnosis framework, which can automatically learn and discover complex and deep connection modes among a plurality of brain areas so as to construct a dynamic brain network. According to the method, a global structure of a brain network is embedded into brain network construction based on a time window through a window mapping module, so that richness and accuracy of brain network topology feature representation are improved; in the fusion process of window features, a learnable time graph convolution module is provided to automatically capture time connectivity across time windows and effectively integrate high-order dynamic topological features extracted from different time windows. And finally, disease diagnosis and classification are realized by using a multi-layer perceptron. According to the method, the identification and classification accuracy of the neurodegenerative diseases under the fMRI data can be effectively improved.

The invention discloses a method for identifying an autism brain image through multi-mode brain image information fusion, is applied to the field of image identification, and aims to solve the problems that multi-mode nuclear magnetic resonance image data are not fully utilized and the model interpretability is insufficient in the prior art. The method comprises the following steps: respectively extracting brain region features from three modal data of a structural magnetic resonance image, a functional magnetic resonance image and a phenotype, then carrying out multi-modal fusion, and constructing a brain region map structure; designing a drawing convolution module and a loss function to learn how to extract brain map depth features; on the basis of brain map depth features, a multi-layer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; the trained multi-mode brain image fusion identification autism model can be used for assisting autism diagnosis and identification.

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

The invention relates to the technical field of brain age prediction and image data processing, in particular to a brain age prediction method, system and device based on multi-modal information fusion and a medium. Multi-scale enhancement processing is carried out on a preprocessed image, multi-scale features and space attention information are combined, a brain image feature map is obtained, and a brain age prediction result is obtained; different feature extraction modes are respectively used for the brain region node feature matrix and the adjacent feature matrix, a plurality of different image convolutions are used for carrying out parallel convolution processing to extract brain region node features, an edge convolution processing and row convolution column convolution processing combined mode is used for extracting adjacent features, a brain image omics feature map is obtained after splicing, and the brain region node features are extracted. And further performing feature splicing and cross-modal feature fusion processing on the brain image feature map and the brain radiomics feature map to realize accurate prediction of the brain age.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses an artificial intelligence multi-modal medical image processing diagnosis and treatment system, and relates to the technical field of medical image processing and diagnosis and treatment, and the system collects multi-angle multi-modal brain image data, extracts brain region features based on multi-modal brain images, formulates a brain normal model, carries out the set fusion of the obtained brain region features, and carries out the diagnosis and treatment of the brain normal model. Generating a three-dimensional structure model of the brain area, compensating the three-dimensional structure model by using the treatment data of the patient to obtain patient features, and analyzing the patient features to obtain a diagnosis report; according to the method, the brain three-dimensional structure model is constructed by fusing the multi-modal image features and the patient treatment data, and the pathophysiological process of the brain diseases is simulated, so that the diagnosis accuracy and reliability are improved, and technical support is provided for early diagnosis and personalized treatment of the brain diseases.

The invention belongs to the technical field of nerve regulation and control, and discloses a portable noninvasive brain deep electrical stimulation system based on time interference, and the system comprises an electroencephalogram signal collection unit which is used for obtaining an electroencephalogram signal in a high-quality resting state, and carrying out the signal preprocessing and feature extraction, thereby obtaining an optimal stimulation envelope frequency; the personalized finite element modeling unit is used for constructing a head personalized model and outputting optimal electrode configuration and stimulation parameters for a target brain region target spot; the electrode positioning and stimulation execution unit is used for realizing accurate positioning of an electrode position and implementing time interference electrical stimulation; the MCU unit is used for realizing data management, task scheduling and remote communication; the human-computer interaction unit is used for realizing visual treatment flow and parameter setting; and the power supply management unit provides stable working voltage for the system. According to the invention, the portable, intelligent and personalized modeling of the electrical stimulation treatment equipment is realized, and the precision and efficiency of nerve regulation and control are remarkably improved.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.

The invention discloses an Alzheimer's disease early screening method based on magnetic resonance imaging, and relates to the technical field of magnetic resonance imaging, and the method comprises the steps: obtaining information identity data and magnetic resonance scanning image data of a to-be-detected person; preprocessing the magnetic resonance scanning image data, and calculating a brain area volume and a DTI-ALPS index according to the preprocessed magnetic resonance scanning image data; constructing a brain network and calculating global network attributes and regional node attributes according to the brain network; substituting the information identity data, the brain area volume, the DTI-ALPS index, the global network attribute and the area node attribute of the to-be-tested person into an Alzheimer's disease screening model to calculate diagnosis parameters; and comparing the diagnosis parameter with the optimal diagnosis threshold value to obtain a screening result. According to the method, the brain colloidal lymphatic network function is evaluated by means of multiple high-sensitivity nerve image indexes, and the early screening capability of the Alzheimer's disease is improved.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention discloses a brain data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal image data of the brain of a target object, the multi-modal image data at least comprising resting state functional magnetic resonance imaging data and diffusion tensor imaging data at a plurality of collection moments; for each brain region of the brain, a brain region dynamic model of the brain region is constructed according to the diffusion tensor imaging data and the resting state functional magnetic resonance imaging data at the multiple acquisition moments, and the brain region dynamic model comprises disturbance parameters; by adjusting disturbance parameters of a brain region kinetic model of the brain region, simulation time sequences of the brain region under the multiple disturbance parameters are obtained, critical indexes of the brain region are determined according to the multiple simulation time sequences, and a critical toughness coefficient of the brain region is determined according to the critical indexes under the multiple disturbance parameters; constructing a critical toughness map of the brain according to the critical toughness coefficients of the plurality of brain regions, and displaying the critical toughness map; therefore, the brain health state is quantitatively evaluated.

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

The invention discloses a motor imagery difficulty regulation and control method and device based on a brain-computer interface and a storage medium. Electroencephalogram signals of a plurality of brain areas of a user are collected, and an average power spectral density curve of the electroencephalogram signals of each brain area is extracted; extracting relative intensity of alpha rhythm energy and beta rhythm energy, an energy peak value in an alpha rhythm frequency range and an energy peak value in a beta rhythm frequency range according to the average power spectral density curve; according to the relative intensity of the alpha rhythm energy and the beta rhythm energy and each energy peak value, utilizing the classification model obtained by training to predict and obtain the imagination accuracy of the user executing the motor imagination task; a motor imagery difficulty level is determined based on the imagery accuracy. Through refined feature extraction, the accuracy and stability of the classification model can be improved; and the extracted features are input into the trained classification model, so that the accuracy of imagination can be quickly and accurately obtained, and the difficulty level of motor imagination is further determined.

The invention belongs to the field of electroencephalogram signal analysis, and particularly relates to an asynchronous EEG-fNIRS-oriented brain image signal causal analysis method and device, and the method comprises the steps: creating a psychological experiment normal form for guiding a patient to carry out a cognitive ability test; collecting and preprocessing asynchronous EEG and fNIRS signals generated by the patient in the process of executing the psychological experiment normal form; extracting spatial features from the fNIRS signals to process EEG signals, so that the two signals are spatially aligned; time sequence features are extracted from the EEG signals to process the fNIRS signals, so that the time sequence features and the fNIRS signals are aligned; and analyzing the causal relationship between the EEG and the fNIRS signal after space and time alignment. According to the method, the spatial-temporal information of various modal signals collected by asynchronous non-joint equipment is fully utilized, the flexibility and accuracy of the method can support deep analysis of brain region states, and an effective tool is provided for neurovascular coupling analysis and clinical application of EEG and fNIRS.

The invention relates to the technical field of condition prediction, and particularly discloses a condition score prediction method for Alzheimer's disease, which comprises the following steps: S1, acquiring longitudinal multi-time-point MRI image data and corresponding clinical score data of a target subject; s2, processing to obtain a feature matrix of the brain region and brain region data and clinical scores at different time points; s3, performing sparse representation on the feature matrix by using a dictionary learning algorithm to obtain a sparse representation matrix; s4, constructing an SLSTM, taking the sparse representation matrixes of the baseline moment and the M06 and M12 time points as input, and training a model to predict clinical scores of subsequent time points M18, M24 and M36; according to the method, sparse representation is performed on MRI image data through dictionary learning, redundant features are removed, features highly related to disease change are extracted, and a long-term dependency relationship in a longitudinal time sequence is captured in combination with a double-layer stacked LSTM structure, so that the model can more accurately predict clinical scores of future time points.

The invention discloses a multi-target transcranial electrical stimulation device based on cross-regional phase-locked stimulation, and the device comprises a light electroencephalogram cap which is integrated with at least one pair of electroencephalogram signal collection electrodes located in a first brain region and used for monitoring specific neural oscillation, and is provided with electrode holes used for placing stimulation electrodes in a second brain region; a plurality of stimulating electrodes; a controller, a multi-channel stimulation module, an electroencephalogram signal processing and feedback regulation and control module and a communication module are arranged in the control box; the electroencephalogram signal module is configured to detect the phase of specific neural oscillation of a first brain region in real time; the controller is configured to accurately control the time or phase of stimulation (such as transcranial alternating current stimulation or pulse stimulation) output by the multi-channel stimulation module in the second brain region based on the detected real-time phase information, and cross-region phase-locked stimulation is achieved. The device is suitable for personalized nerve regulation and control application in a family environment while remarkably simplifying user operation, reducing cost and improving comfort and portability.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention relates to a deep learning and emotion recognition technology, in particular to a two-way electroencephalogram emotion recognition method and system combining general path learning and medical priori. The method comprises the following steps: preprocessing an electroencephalogram signal, and extracting a difference entropy feature; constructing an adjacent matrix AC based on a general path and an adjacent matrix AR of a brain region divided based on medical priori; taking the difference entropy features as node features, respectively taking the adjacent matrixes AC and AR as edges, and constructing a path graph structure and a prior graph structure; performing graph convolution operation on the path graph structure and the prior graph structure to update node features according to the adjacency matrixes AC and AR; fusing the two paths of updated node features to obtain high-dimensional fusion feature matrix representation; and the electroencephalogram emotion is identified and classified through a multi-layer perceptron. According to the method, general emotion recognition features and biological prior of brain region division are effectively combined, and more comprehensive and accurate feature representation is provided for emotion recognition.

The invention belongs to the field of artificial intelligence and electroencephalogram emotion recognition, and provides a double-branch electroencephalogram emotion recognition method and system based on brain region topology and time-space, and the method comprises the steps: preprocessing a to-be-recognized electroencephalogram signal to obtain a plurality of electroencephalogram fragments, and extracting a difference entropy sequence of each electroencephalogram fragment and a Spearman correlation coefficient matrix between channels; based on the Spearman correlation coefficient matrix, utilizing a bridging dynamic graph attention network module to extract topological features of a brain region; processing the differential entropy sequence by using a multi-scale space-time mixed attention module to obtain multi-scale space-time features; carrying out residual mutual cross attention fusion on the topological features of the brain region and the multi-scale spatial-temporal features to obtain fusion features; and performing classification based on the fusion features, and determining an emotion recognition result corresponding to the electroencephalogram signal. According to the method, the accuracy and robustness of emotion recognition are improved by utilizing the spatial topology characteristics and the multi-topology time dynamic characteristics of the electroencephalogram signals, and the defects of modeling spatial dependence and time dynamic are overcome.

The invention provides a transcranial alternating current stimulation method and system with an automatic adjustment function, and relates to the technical field of transcranial alternating current stimulation, and the method comprises the steps: obtaining, compressing and sampling a multi-channel electroencephalogram signal, carrying out the multi-channel reconstruction through the physiological priori and the related information of adjacent brain regions, extracting the feature parameters of target brain waves, and carrying out the reconstruction of the target brain waves. Based on the parameters, dual-stage transcranial alternating current signals of prior stimulation and post stimulation are output; by introducing an adjacent matrix or a graph Laplacian operator in a compressed sensing acquisition and reconstruction link, high-fidelity recovery of cross-brain region oscillation can be maintained under the conditions of low sampling rate and noise; performing layered screening and dynamic fine tuning on characteristic frequency bands of different brain regions by combining general and individualized prior; the target brain wave phase is locked in advance in the first stimulation stage, and the stimulation intensity of each brain region is independently or cooperatively adjusted in the later stimulation stage, so that the requirement of coupling regulation and control of multiple brain regions is met, and the closed-loop intervention effect on nerve states such as insomnia and anxiety is improved.

The invention discloses a functional connection network analysis method based on a group sparse attention converter. The method mainly comprises the following steps: preprocessing brain functional magnetic resonance imaging data output by a functional magnetic resonance instrument; dividing the preprocessed brain function image into a plurality of brain regions according to a standard; based on Pearson correlation and group sparse characteristics, calculating a dependency relationship between brain regions, and respectively constructing an initialized brain network and a guidance network; taking the initialized brain network as the input of a group sparse attention converter, and carrying out learning under the assistance of a guidance network so as to learn a brain network with a group similar topological structure; and subsequent classification is carried out. The method integrates priori knowledge of a population level, allows more comprehensive modeling across multiple brain networks, is designed for end-to-end adaptive learning, dynamically updates the brain networks through back propagation, and significantly improves the construction and analysis performance of the brain networks.