249 results about "Functional connectivity" patented technology

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

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 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.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention discloses a brain age prediction method based on structure and functional MRI image multi-modal fusion, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting spatial structure characteristics of a structural magnetic resonance image by using DenseNet121; meanwhile, a function connection matrix is constructed according to the time sequence of the functions, a graph structure is constructed on the basis of the matrix, the characteristic of each node is the connection strength between the node and other nodes, and the edge is converted into sparse graph representation from the absolute value of the connection strength; then extracting functional features by using a graph attention network, and fusing the structure and the functional features by using a cross attention mechanism; and applying a gating mechanism fusion result to a brain age prediction regression task. According to the brain age prediction method, complementary information of multi-modal data is fully utilized, and biological markers of brain aging can be accurately captured.

The invention discloses a transcranial magnetic stimulation positioning system based on a deep effect brain region, and the system comprises the steps: obtaining the data of a tested magnetic resonance image, which comprises a resting state functional image and a high-resolution T1 structure image; performing data preprocessing on the function image and the structure image, and establishing an individual space; the deep effect brain region template is converted to an individual space through nonlinear registration, and a target region of interest is defined; calculating functional connections between voxels of the deep effect brain region and the cortical region by adopting a Granger causality analysis method based on wavelet transformation; and screening Top block masses according to the functional connection strength, calculating the gravity centers of the Top block masses, and determining individualized stimulation targets in combination with the skull surface distance. According to the method, spontaneous brain activity function variation of the brain of an individual and the brain signal propagation direction are fully considered, accurate positioning of different individual levels is achieved, relatively stable cortex stimulation targets are obtained, and therefore the clinical curative effect of TMS is optimized.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

The invention belongs to the field of brain-computer interfaces, and particularly relates to a decision-making brain-computer method and device based on virtual reality induction, which combines two psychological decision-making tasks of auditory stimulation and visual stimulation and utilizes virtual reality equipment to create a decision-making brain-computer interface normal form of panoramic interaction, so that a subject can fit a scene facing a decision in reality to the greatest extent, and the accuracy of decision making is improved. The sensory motor cortex is effectively activated, and cooperative activation of the cognitive-motor neural network is induced; a decision interaction feedback link is added to enhance a decision stimulation effect and a cranial nerve feedback mechanism by analyzing related characteristics P300, power spectral density and brain network function connection quantity of the acquired electroencephalogram signals during normal form execution decision reaction. Compared with the prior art, the method has the advantages that immersive audio-visual stimulation and task interaction feedback are brought into a decision-making brain-computer interface for the first time, and a new scheme with neural rehabilitation and human-computer interaction functions is provided for neural feedback training of cognitive functions.

The invention discloses a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention provides a network graph generation method based on brain image data, and relates to the technical field of brain connection.The method comprises the steps that brain image data (including fMRI data and sMRI data) of a target object are obtained, the brain image data are preprocessed and registered, and registered brain graph data are obtained; dividing the brain map data into a plurality of brain regions based on a set brain map, and determining time sequence data of each brain region; based on the time sequence data of each brain region, a function connection matrix is determined, and non-diagonal elements in the function connection matrix reveal the connection strength between the brain regions; and based on the brain region and the functional connection matrix of the brain map data, generating a brain network map (which can be visualized) of the target object. According to the scheme, the sMRI data (the precision is millimeter level) is introduced as a registration basis, so that the registration precision can be improved, and a brain network diagram with higher precision can be generated subsequently.

The invention belongs to the technical field of artificial intelligence and physiological signal processing, and discloses an electroencephalogram emotion recognition method and system based on a multi-task self-supervision and dynamic graph fusion network, and the method comprises the steps: obtaining and preprocessing electroencephalogram and other physiological signals, and extracting multi-band energy features; constructing a dynamic graph structure, taking electrodes as nodes, taking frequency band energy as characteristics, and fusing spatial distance and functional connectivity to generate a dynamic adjacency matrix; designing multi-task self-supervised pre-training, including spatial jigsaw, frequency jigsaw and cross-modal contrast learning tasks, to learn general characterization; a dynamic graph fusion network is adopted to carry out end-to-end training, and a shared feature extraction module of the dynamic graph fusion network realizes adaptive fusion of multi-modal features by utilizing Chebyshev graph convolution and embedding a cross-modal attention mechanism; the classification module sets an independent classification head for each task, and optimization is carried out through a joint loss function. According to the method, the accuracy and generalization ability of electroencephalogram emotion recognition are remarkably improved.

The invention discloses a brain function magnetic resonance imaging data analysis method based on a contrast graph neural network, and the method comprises the steps: firstly carrying out the data enhancement of a brain function connection graph, and simulating the heterogeneity of brain function magnetic resonance data, so as to improve the diversity of a data set; secondly, the hidden space embedding features of the brain function connection diagram are efficiently learned by fusing a double-Hough-Laplacian diagram convolutional network and a contraction incentive mechanism; the embedded features are mapped to a group of prototype vectors, and prototype allocation codes corresponding to the embedded features are calculated by adopting a Sinkhorn-Knopp algorithm; performing exchange optimization on prototype codes between different enhanced brain connection diagrams of the same subject through a contrast learning strategy, and compelling codes of homologous subjects to be aligned at the minimum cost in combination with a cross entropy loss function; and finally, applying the pre-training model to a functional magnetic resonance imaging data set of the Alzheimer's disease, and carrying out interpretability analysis on learning features to improve the classification efficiency and pathological analysis of the Alzheimer's disease under a limited tag condition.

The present invention belongs to the field of rehabilitation therapy technology and discloses a longitudinal analysis method and system for magnetic resonance imaging data of mild brain injury. The method extracts BOLD signals from the magnetic resonance imaging data of subjects; constructs a symmetric positive definite sparse brain functional connectivity network set of subjects based on sparse inverse covariance matrix estimation; determines the brain functional connectivity network dictionary and sparse coefficient matrix in kernel space based on Riemannian manifold sparse coding; performs spatial distribution analysis of brain functional connectivity atomic networks; and performs longitudinal analysis of magnetic resonance imaging data of mild brain injury. By analyzing the differences in the spatial distribution of these highly present brain functional connectivity atomic networks in the brain, the present invention digs out brain functional connectivity imaging markers for distinguishing the three mild brain injury rehabilitation treatment stages: acute phase, subacute phase, and complete recovery, thereby realizing longitudinal analysis of the mild brain injury rehabilitation process.

The invention discloses a method for detecting interpersonal nerve synchronization under audio-visual stimulation, which comprises the following steps of: designing a double-person super-scanning experiment normal form, collecting double-person electroencephalogram signals, and constructing a database of visual stimulation and auditory stimulation; preprocessing the data in the database to obtain electroencephalogram signals of four different frequency bands delta, theta, alpha and beta, extracting the electroencephalogram signals of the alpha frequency band, segmenting the electroencephalogram signals, and calculating a correlation ISC value between subjects of each segment; an intra-brain network and an inter-brain network are constructed by constructing a functional connection matrix, and the similarity of the intra-brain network and the global efficiency of the inter-brain network are calculated, so that the cooperation and synchronization degree between neural activities of subjects under visual stimulation and auditory stimulation is effectively evaluated. The invention further discloses a system for detecting interpersonal nerve synchronization under audiovisual stimulation. According to the method, the difference of subjects is reduced by standardizing experimental conditions, and the influence of single sensory stimulation on an intracerebral network activation mode is studied.

The invention discloses a three-dimensional brain network dynamic segmentation method based on deep learning. The method comprises the following steps: S1, constructing fused image volume data with consistent time and consistent space; s2, a multi-scale sparse Transform coding feature pyramid is generated; s3, obtaining a same-scale brain region map structure; s4, taking the cross-scale node alignment fusion graph structure as initial output of a cross-scale node alignment fusion mechanism; s5, obtaining a first round of fusion brain region graph node embedding feature; s6, an updated multi-scale sparse Transform coding feature pyramid is obtained, and the step S3 to the step S5 are repeated until interaction updating of the multi-scale sparse Transform-GNN is completed; and S7, obtaining a three-dimensional brain region dynamic segmentation result. According to the method, collaborative extraction of local fine granularity and global coarse granularity features is realized, redundant information can be effectively inhibited, and the modeling capability of spatial structure and functional connection features in a cross-modal fusion image can be enhanced.

The invention provides a working memory ability assessment method and device for dynamic brain network attention fusion, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: segmenting a BOLD signal time sequence in functional magnetic resonance imaging data, extracting time sequence data of a region of interest, and constructing a functional connection matrix; extracting and fusing spatial dependence and time fluctuation characteristics in the functional connection matrix based on an attention mechanism to obtain a full connection graph; clustering the full connection graph by using multi-head attention to obtain a functional magnetic resonance imaging embedded representation of each testee; training the prediction network by using the functional magnetic resonance imaging embedding representation of the testee; and using the trained prediction network to complete work memory evaluation of the target subject. According to the method, dynamic multi-graph fusion and neuroscience priori knowledge are combined, individual differences and specific noise are eliminated through comparative learning, accurate evaluation of the working memory ability is achieved, and an objective basis is provided for evaluation of the cognitive function.

The invention relates to the field of medical image analysis and neuropsychiatric disease diagnosis, and particularly discloses a brain network typing diagnosis system and method for attention deficit hyperactivity disorder. Comprising a data acquisition and preprocessing module, a topological feature extraction module, a supervised manifold learning module, a network reconstruction and diagnosis module, a parameter optimization module and a result verification module, continuous homology analysis is performed on a brain region function connection matrix based on an algebraic topology theory, and a brain region topological feature matrix is generated; utilizing supervised manifold learning to map the brain region topological feature matrix to a Riemannian manifold, and calculating a brain region importance weight map based on ADHD phenotypic features as supervised signals; according to the method, the brain network is subjected to subtype specific reconstruction through the curvature flow theory of Riemannian geometry, the topological difference between different subtypes is calculated, accurate typing diagnosis of ADHD is achieved, and the diagnosis accuracy is improved by 15%-20%.

The invention relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses a personalized electroencephalogram emotion recognition method and system based on dynamic brain region division, and belongs to the technical field of electroencephalogram emotion recognition. The method comprises the steps that electroencephalogram signals are preprocessed, and an electroencephalogram signal matrix is constructed; on the basis of the preprocessed electroencephalogram signals, five-frequency-band differential entropy feature values are extracted, multi-head self-attention calculation is carried out in combination with rotation position coding, and channel feature representation with strong emotion intensity is output; on the basis of the constructed electroencephalogram signal matrix, dynamically dividing a brain function region by using a CNM algorithm, optimizing the structure of the brain function region by using a modularity value, and outputting brain region characteristics; and fusing the channel feature representation and the brain region features, predicting an emotion tag through a graph neural network, and outputting an emotion recognition result. According to the method, the individual brain function region can be dynamically divided, the function integration information of the brain region is effectively captured, and deep fusion of brain region function connection and channel emotion information is realized.

The invention relates to an autism classification method based on a multi-scale residual image neural network, and aims to cope with the challenge of crowd autism classification in multi-modal medical data. The method comprises the following steps: firstly, providing a new function connection feature construction method, extracting second-order function connection features by using tangent Pearson embedding to capture a high-order interaction relationship between brain intervals, and then adopting a maximum independent domain to adaptively minimize statistical dependence between the features and acquisition sites, and combining F-score to screen the features with the most discriminative ability, so as to obtain the feature with the most discriminative ability. And redundancy is effectively removed. Secondly, a multi-modal edge weight calculation method fusing imaging information and non-imaging information is provided, so that noise interference is effectively suppressed while a key discriminant relation is reserved. And finally, expanding a node receptive field layer by layer by stacking multiple layers of Chebyshev convolutions with residual errors on the subject graph so as to capture multi-level relation characteristics, and performing weighted modeling and adaptive fusion on convolution output of each layer by using a multi-head self-attention mechanism, so that effective integration of multi-scale information is realized, and accurate classification of autism is realized. The method has excellent performance in the aspect of autism classification, and an innovative, feasible and effective solution is provided for solving the autism classification task in the multi-modal medical data.

The invention relates to an epilepsy electroencephalogram detection method adopting multi-modal feature fusion and an attention mechanism, and belongs to the technical field of medical signal processing and artificial intelligence. According to the method, automatic detection of epilepsy is realized through three core modules: firstly, preprocessing and time-frequency transformation are performed on electroencephalogram signals, a time-frequency graph is generated, and power spectrum characteristics of different frequency bands are extracted; the method comprises the steps that firstly, a time-frequency diagram and a function connection diagram are processed at the same time through a multi-modal feature combined extraction network, and finally fusion enhancement and classification decision making of multi-modal features are achieved through a self-adaptive feature fusion and classification network in combination with a learnable frequency band attention mechanism and a Transform encoder. According to the method, the time-frequency characteristics, the brain region connection relation and the long-range dependency relation of the electroencephalogram signals can be captured at the same time, the limitation of a traditional method in the aspects of feature expression and mode recognition is solved, and the accuracy of epilepsy detection is remarkably improved.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

The invention discloses an infantile autism spectrum disorder identification system based on a graph neural network, and the system specifically comprises a data processing module which is used for extracting time sequence information from fMRI data and constructing a brain function connection graph; the model training module is configured with an unsupervised graph auto-encoder GAE and comprises a three-layer graph convolutional network GCN as an encoder and an inner product decoder; the biomarker recognition module is used for executing a replacement test and screening brain regions with significant differences; and the performance evaluation module is used for evaluating the diagnostic performance of the system and the effectiveness of biomarker discovery on the ABIDE data set. The invention provides an ASD identification solution which does not need manual annotation, is high in interpretability and excellent in performance, and has important significance for promoting the development of ASD early diagnosis and personalized treatment.

The invention discloses a DTI-based brain network analysis method for a refractory OAB patient, and relates to a method for applying diffusion tensor imaging (DTI) and graph theory analysis to explore a central nervous regulation mechanism of the refractory OAB patient. 43 cases of refractory OAB patients and 46 cases of matched healthy contrasts are selected for DTI scanning. The method comprises the following steps: evaluating an overactive bladder symptom score table (OABSS), an overactive bladder symptom questionnaire table (OAB-Q), a Hamilton anxiety scale (HAM-A) and a Hamilton depression scale (HAMD) of all subjects, and recording related clinical data. A DTI and graph theory analysis method is adopted to explore the change of global and local topological attributes of the brain structure network of the intractable OAB patient. And further performing brain network function connection analysis on the discovered differential brain region as a seed point.

The invention relates to a gradient-based tactile topology mapping model construction method. The method comprises the steps of obtaining functional imaging data and structural imaging data of magnetic resonance scanning of a testee; constructing a voxel-level functional connection matrix based on a voxel space in the functional imaging data; constructing a vertex-level structure connection matrix based on a cortex space in the structure imaging data; based on a gradient dimension reduction analysis processing method, matrix similarity calculation and dimension reduction analysis are carried out on the function connection matrix and the structure connection matrix, and a plurality of gradient components and variance interpretation quantities are obtained; and on the basis of the variance interpretation quantity, determining a functional main gradient and a second structural gradient, and constructing a tactile topology mapping model in combination with a tactile topology distribution rule. According to the method, the problem that the current tactile perception topological mapping is not accurate is solved, and more accurate mapping of tactile perception and brain activation is realized.

The invention discloses an optimization method for senile cognitive impairment intervention. The optimization method comprises the following steps: S1, a multi-modal evaluation stage: acquiring neurophysiology, behavior and language data of a patient through a multi-source sensing module; s2, personalized prescription generation: calculating a comprehensive cognitive impairment index (CCI), and generating a personalized cognitive training scheme; s3, the intervention module executes the steps that a virtual training scene is generated in the AR environment; and S4, effect verification and iteration: generating a neuroplasticity evaluation report after training, and quantifying the parahippocampal gyrus-prefrontal cortex function connection enhancement degree. Neurophysiology, behaviors and language data of a patient are synchronously collected by adopting a multi-source sensing module, and a personalized cognitive training scheme is generated based on a comprehensive cognitive disorder index. Meanwhile, a virtual scene is constructed to execute an intervention module, and the parahippocampal gyrus-prefrontal lobe function connection enhancement degree is quantitatively analyzed after intervention, so that an evaluation-decision-intervention-verification closed-loop system is constructed, and the intervention effect of the senile cognitive impairment is remarkably improved.

The invention discloses a method for determining an OAB target based on brain network characteristics. The method comprises the following steps: a) detecting the functional connection strength of the right forehead cortex of a patient in a resting state through functional magnetic resonance imaging; b) identifying significantly weakened brain region connections, including paracentral lobules, cerebellar lower feet and marginal systems, as compared to a healthy control group; c) determining the number of white matter fiber bundles with abnormal structural connection in the brain interval by combining a diffusion tensor imaging fiber tracking technology; and d) selecting a brain region with abnormal function and structure connection as a nerve regulation treatment target.

The invention discloses an electroencephalogram and myoelectricity heterogeneous graph action recognition method based on dynamic graph optimization and functional adjacency fusion, which comprises the following steps: firstly, constructing a weighted fusion adjacency matrix of electroencephalogram (EEG) and myoelectricity (sEMG) channels by utilizing multiple functional connection indexes (including a phase locking value, a weighted phase lag index and mutual information) to form a heterogeneous graph structure; a learnable channel weight mechanism is introduced, redundant channel pruning is realized through sparse regularization, the expression efficiency of a graph structure is optimized, then local and global features of a fusion graph are extracted based on graph attention, and a comparative learning guide model is utilized to learn consistency and discriminative features between modes. The method can effectively improve the generalization ability and action recognition precision of electroencephalogram and myoelectricity joint modeling, and has good robustness and interpretability in a multi-channel brain-computer interface system.

The invention belongs to the technical field of image processing, and discloses a schizophrenia classification method based on connection gradient, which comprises the following steps: acquiring magnetic resonance imaging data, and preprocessing the magnetic resonance imaging data; performing brain region division on the preprocessed magnetic resonance imaging data, and constructing a functional connection network and a morphological similarity network; constructing a function connection gradient and a form similarity gradient by using a connection gradient algorithm based on the function connection network and the form similarity network; performing threshold processing on the function connection network and the morphological similarity network to serve as edge features, taking the bimodal connection gradient as node features, and constructing a graph convolutional network classification model; and training and testing the graph convolutional network classification model. According to the schizophrenia classification method based on the connection gradient, schizophrenia classification identification is realized by using the graph convolutional network classification model, the classification accuracy is improved, and the application value is higher.

The invention provides an EEG function connection prediction method based on fMRI deep cross-modal representation learning, and relates to the crossing field of neuroiconography and artificial intelligence. According to the method, a sample pair is constructed through time alignment of EEG and a blood oxygen level dependent signal, an end-to-end deep neural network architecture composed of a time projection unit, a cross-attention fusion encoder and a connection synthesis head is designed, and direct mapping from a BOLD signal to EEG function connection is achieved. The model adopts a composite loss function, and gives consideration to the consistency of the prediction connection matrix with the real EEG function connection in numerical precision and topological mode, thereby achieving the high-fidelity reconstruction of the EEG function connection map at the frequency domain level. According to the invention, the topological structure stability of the brain function network can be effectively maintained. Under the conditions of incomplete EEG data, serious noise interference or complete loss, the frequency domain function connection characteristics can still be stably recovered, and a reliable analysis substitution path is provided for brain function network research.