161 results about "Functional connectivity" patented technology

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.

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 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 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 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 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.

The application discloses a kind of group level fMRI brain function network analysis methods based on graph convolutional neural network, comprising: obtaining the fMRI of the brain of multiple groups of different categories of subjects, after preprocessing, establish brain function network for each subject, carry out single sample t test to each group brain function network, calculate the graph theory attribute of node as node feature;Establish and train the classification model of GCN, the input of neural network is the edge of brain function network, node feature, and the output is the category of subject;According to the explainability of GCN, find the most important subgraph structure for classification, the subgraph represents the biggest brain function connection of the difference of brain function network between different groups, and can be used for further analysis on the neural mechanism of brain disease.The application can more comprehensively compare the brain function network of different groups, obtain more accurate results, and can be widely used in aphasia, depression, alzheimer's disease and other brain function network analysis of brain disease.

This invention discloses a method for classifying consciousness disorders in EEG signals based on dynamic graph convolution and channel attention mechanisms, relating to the field of EEG signal recognition technology. According to the method provided in the embodiments of this invention, a complete closed loop is achieved, covering uploading, preprocessing, artifact removal, segmentation, feature extraction, and model prediction. A dynamic graph convolution modeling method with a trainable adjacency matrix is ​​used to adaptively learn functional connections between EEG channels, overcoming the poor generalization problem of static adjacency matrices. Simultaneously, a joint modeling framework of explicit connectivity (PLV) + implicit connectivity (dynamic graph convolution) is used to more robustly capture cross-channel synchronization patterns under low signal-to-noise ratio conditions.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The invention provides a mental disease classification method based on a two-stage multi-atlas neural network, and belongs to the technical field of medical information intelligent diagnosis. The technical problems that early-stage features are excessively mixed due to multi-atlas information interaction in the prior art, unique disease-related specific characterization of each atlas can be diluted, and potential noise in cross-atlas connection can be possibly amplified are solved. The method comprises the following steps: firstly, on the basis of fMRI data, constructing a functional connection matrix by using various brain maps, and constructing cross-map edges through spatial proximity to obtain a joint map; then, adopting a two-stage graph neural network alternate propagation mechanism: only starting a graph inner edge to extract stable features in an odd number layer, starting a cross-graph edge in an even number layer, and realizing multi-graph information fusion in combination with an action mechanism; and finally, realizing mental disease prediction through graph-level pooling and a classifier. According to the method, premature aliasing of map features can be effectively avoided, and the rationality and robustness of cross-map information interaction are enhanced.

The application discloses a kind of fNIRS motor imagination decoding methods based on double-flow cross attention and function connection fusion, obtain the fNIRS original signal of subject under motor imagination task, calculate HbO signal and HbR signal concentration variation sequence, respectively to HbO signal and HbR signal are first-order differential processing, generate the enhanced feature stream reflecting the change rate of HbO signal and HbR signal, and introduce residual connection;Double-flow feature encoder is constructed, and HbO signal depth feature map and HbR signal depth feature map are extracted using time convolution and depth space convolution respectively;Cross attention module is introduced, and the depth feature after double-flow dynamic complementary fusion is obtained;For each signal sample obtained, calculate the pearson correlation coefficient matrix between all acquisition channels, and flatten it into a global brain network connection vector matrix;The depth feature after double-flow dynamic complementary fusion is spliced with the global brain network connection vector matrix, and the category result of motor imagination is output through fully connected classifier.

The application provides a personalized rehabilitation path making method and system based on amygdala function evaluation. First, the application obtains multi-modal neural function data of an individual. Second, the amygdala function lateralization mode is determined based on amygdala sub-region functional connection data. Third, the amygdala function lateralization mode is cooperatively analyzed with preset target individual behavior representation data. Finally, the personalized rehabilitation path is generated according to the cooperative analysis result obtained by the analysis. The technical scheme provided by the application not only realizes sub-region level accurate evaluation of amygdala function by integrating multi-modal neural function data and individual behavior representation data, but also generates a highly individualized rehabilitation path with precise coupling of neural activity and rehabilitation training, thereby effectively improving the pertinence and effect of intervention.

The embodiment of the application discloses a kind of Alzheimer's disease classification and key brain area determination method based on random attention and counterfactual contrast learning, it is related to medical image analysis technical field;Alzheimer's disease classification and key brain area are determined by the functional magnetic resonance imaging data of subject;The accuracy, overall performance and stability of the graph convolution network model are significantly better than the existing GNN baseline model in the AD vs.NC task of ADNI real data set;The output key brain area node is highly consistent with clinical medicine priori;Specific brain area and functional connection leading to abnormal classification can be intuitively presented;It conforms to the real pathological mechanism, and whether the verification model in the same framework is really highly dependent on the selected key brain area structure, so that the explanation result can be accepted by clinician in the medical scene with extremely high safety requirement.

The invention relates to the technical field of medical image analysis and brain network research, in particular to a glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises the steps of obtaining and preprocessing fMRI and structural MRI data of a patient, eliminating a tumor area through a focus shielding registration strategy, and reducing mass effect interference; dividing a tumor core region, a peritumoral abnormal region and a normal brain region; a Yeo-17 network template is registered to an individual brain region to realize mapping; defining a tumor core area as an independent network unit, and forming a new set with 17 normal networks; calculating the connection strength of whole brain voxels and network functions, and judging functional connection voxels according to a threshold value; and quantifying the intratumoral function ratio RIFR and the peritumoral ligation ratio RPTR, and generating a visual map. According to the method, an individual brain function network is accurately mapped, tumor heterogeneity interference is overcome, repeatable quantitative indexes are provided, and a reliable imaging analysis tool is provided for brain glioma function protection and clinical research.

The application provides a driving intention recognition method based on functional connectivity and a graph neural network, comprising the following steps: S1, collecting electroencephalogram signals of a driver during driving, and preprocessing original electroencephalogram data; S2, calculating power spectral densities of each frequency band of the preprocessed electroencephalogram signals as electroencephalogram signal frequency domain features; S3, constructing an adjacency matrix as an initial graph structure based on electrode spatial proximity and functional connectivity; and S4, inputting the frequency domain features in S2 and the adjacency matrix obtained in S3 into a graph attention network for feature aggregation, inputting the extracted feature expression into a classifier to classify driving intentions and outputting results. The method can solve the problems of single electroencephalogram feature extraction and poor expression ability of the classification model in the existing driving intention prediction method, improve the accuracy of driving intention classification, improve the interpretability of the classification results, and better apply the method to driving state perception and auxiliary decision-making of a human-machine co-driving system.

The invention relates to the technical field of medical image analysis, and discloses an Alzheimer's disease analysis system and method based on multi-modal brain network modeling, and the system comprises a data preprocessing module which is used for obtaining original data, and carrying out the data preprocessing of the original data, and obtaining target data; the original data comprises original MRI data and original clinical data; the feature extraction module is used for performing brain region feature processing on the target data based on a multi-map fusion feature processing framework to obtain a brain region feature processing result; the multi-modal modeling module is used for constructing a brain network model recording function connection time-varying characteristic information corresponding to the brain network model according to a brain region characteristic processing result based on a global factor regulation mechanism; and the result output module is used for generating and outputting an interpretable analysis result about the Alzheimer's disease according to the brain network model. Therefore, the analysis accuracy of the MRI data can be improved, so that the analysis accuracy of the Alzheimer's disease is improved.

This application relates to a method, apparatus, and device for screening the risk of reading and attention deficit disorder in children. The method includes acquiring multi-channel raw brain blood oxygenation signals under task-induced conditions using a specific layout fNIRS array integrated into a wearable headband, based on a rapid naming cognitive paradigm. Based on the raw brain blood oxygenation signals, a fusion feature vector representing the reading and attention networks is generated by calculating temporal waveform features and frontotemporal functional connectivity strength. The multi-dimensional fusion feature vector is then processed and analyzed using a Transformer classification model to generate classification results indicating the risk level of reading disorders and comorbid ADHD. This application achieves portable and rapid brain function signal acquisition by integrating a targeted fNIRS array with a standardized cognitive paradigm. By fusing temporal dynamics and brain network connectivity features, a multi-dimensional neural representation is constructed. Finally, a lightweight Transformer model is used to output the risk level of reading disorders and comorbid ADHD end-to-end, achieving high-precision automated assisted screening.

This invention relates to a method and system for EEG emotion recognition based on an adaptive multi-view graph neural network, belonging to the field of brain-computer interface and emotion computing technology. The method includes: dividing multi-channel EEG signals into continuous time windows, and using four adjacent time windows as temporal input samples; extracting multi-band differential entropy features of each time window as initial node features; fusing prior knowledge of electrode spatial proximity and brain biological symmetry to construct a basic matrix, and modulating and applying sparse constraints through a learnable attention mechanism to generate an individualized brain functional connectivity topology; designing a parallel bi-branch deep network, where a graph convolutional branch extracts global spatiotemporal features from the graph structure sequences corresponding to the four time windows, and a one-dimensional convolutional branch extracts and fuses local frequency-spatial features; and during training, comprehensively applying node-level domain adversarial and graph structure collaborative regularization to output the emotion category. This invention is beneficial for improving cross-subject recognition performance.

A kind of schizophrenia electroencephalogram data recognition method based on pulse neural network, collects the electroencephalogram data of schizophrenia patient and healthy control person;The original data are pretreated;Including signal filtering, artifact removal, signal segmentation and data standardization;Extract multi-scale space-time feature, including time-frequency feature, functional connection feature and spatial feature;Including time-frequency feature extraction, functional connection feature extraction and feature fusion three steps;Three-layer pulse neural network is constructed, including input layer, hidden layer and output layer;The connection model between brain partition based on pulse neural network is constructed;Improved back propagation algorithm is used to train network;The difference of brain partition connection mode of schizophrenia patient and healthy person is analyzed.The present application constructs the brain partition connection model of signal distinction of schizophrenia patient and healthy person by simulating the dynamic activity of brain neuron, realizes the improvement of schizophrenia classification accuracy.

This invention discloses a dynamic segmentation method for three-dimensional brain networks based on deep learning. The method comprises the following steps: S1. Constructing temporally and spatially consistent fused image volume data; S2. Generating a multi-scale sparse Transformer encoded feature pyramid; S3. Obtaining the same-scale brain region map structure; S4. Using the cross-scale node-aligned fused map structure as the initial output of the cross-scale node-aligned fusion mechanism; S5. Obtaining the node embedding features of the first round of fused brain region map; S6. Obtaining the updated multi-scale sparse Transformer encoded feature pyramid, and repeating steps S3 to S5 until the multi-scale sparse Transformer-GNN interactive update is completed; S7. Obtaining the dynamic segmentation result of the three-dimensional brain region. This invention achieves the collaborative extraction of local fine-grained and global coarse-grained features, which not only effectively suppresses redundant information but also enhances the modeling ability of spatial structure and functional connectivity features in cross-modal fused images.

The application relates to the cross field of biomedical engineering and artificial intelligence, and discloses a personalized brain power development training method based on electroencephalogram signals. The method comprises the following steps: collecting resting state and task state multi-channel electroencephalogram signals of a subject, constructing a functional connection matrix after pretreatment, identifying individualized weak connection target points through difference operation and cluster analysis; matching a neural feedback training protocol from a preset paradigm library based on the target points, and generating a feedback signal by extracting a target point synchronicity feature in real time during training to guide the subject to actively enhance the weak connection; updating the model after each training and dynamically optimizing subsequent parameters to form a closed-loop regulation. The application improves working memory and attention through individualized targeted training, induces neural plasticity, and realizes efficient and accurate brain power development.

This invention provides a method and system for predicting brain diseases by fusing amplitude and phase information with graph-aware hybrid expert graph neural networks. It relates to the field of neuroimaging analysis technology. The method includes: acquiring raw resting-state functional magnetic resonance imaging (fMRI) data of a subject and corresponding brain disease category labels; preprocessing the raw resting-state fMRI data; extracting the mean oxygenation level dependent signal time series of multiple brain regions of the subject and using the mean oxygenation level dependent signal time series as the signal time series; constructing a functional connectivity matrix and a phase adjacency matrix based on the signal time series; constructing a training dataset; constructing a brain disease prediction model based on a two-branch hybrid expert graph neural network; training the brain disease prediction model using the training dataset; acquiring resting-state fMRI data; inputting the resting-state fMRI data into the trained brain disease prediction model for prediction, and outputting the brain disease prediction result.

A biomarker predictive of a survival outcome of a brain tumor patient is disclosed. The biomarker includes a functional connectivity matrix that includes a plurality of matrix elements. Each matrix element includes a correlation of resting-state fMRI activities of a first and second region of interest from a plurality of regions of interest within the patient's brain. Computing device and systems are disclosed to transform a resting-state fMRI dataset obtained from the patient into the biomarker and to transform the biomarker into a predicted survival outcome using a machine learning model.

This invention discloses a brain disease classification and prediction method and system based on a multimodal topological sensing graph network, relating to the field of medical image processing technology. By fusing temporal signals from functional magnetic resonance imaging (fMRI) and spatial structure signals from diffusion tensor imaging (DTI), a spatial structure prior matrix is ​​introduced as a topology modulator to impose physical connectivity constraints on the constructed multi-view functional interaction network. Furthermore, a spatiotemporally coupled global fusion graph is generated through a cross-modal attention mechanism. Graph neural networks are used to extract local nodes, mesoscale subnetworks, and global topological statistical features to construct a global state feature vector. Finally, a two-stage optimization learning framework is employed, combining multi-objective self-supervised pre-training and supervised fine-tuning to improve the model's classification performance and robustness under small sample conditions. This invention effectively solves the problems of functional connectivity redundancy, lack of structural constraints, and loss of topological information in traditional methods, significantly improving the accuracy and interpretability of brain disease classification.

The invention relates to a method for realizing AFD phase inversion risk prediction based on function connection and a graph neural network, and the method comprises the following steps: preprocessing an image, extracting a time sequence of each brain region based on a predefined brain map, and calculating a whole brain FC matrix; constructing the FC matrix into graph structure data, and inputting the graph structure data into a graph neural network; the Euclidean distance between the feature representation of the AFD patient to be evaluated and the average feature representation of the BD patient population is calculated. The method, the system, the device, the processor and the medium for realizing AFD phase inversion risk prediction based on the function connection and the graph neural network are high in prediction precision, combine GNN with an edge weight attention mechanism, can capture high-order topological characteristics of a brain network, have early warning capability, and can predict and output FC and brain regions which are most critical to decision. The technology fusion innovativeness is high, the brain connection omics, the graph neural network and representation learning are seamlessly fused, and the clinical transformation potential is large.

The present disclosure provides an AI-based clinical decision support system comprising an input module configured to receive clinical information comprising brain scan data, an analysis module configured to parse the clinical information using statistical measures from functional connectivity analysis with counterfactual explanations to identify brain connectivity patterns associated with brain disorders, and an output module configured to present a recommended diagnosis and explanation comprising attribution information identifying connectivity features contributing to the diagnosis. The brain scan data comprises functional magnetic resonance imaging, electroencephalography, and magnetoencephalography data. The statistical measures comprise functional connectivity analysis and graph theory metrics including degree metrics, betweenness centrality measures, and clustering coefficients. The analysis module comprises a functional connectivity engine configured to process brain scan data and generate connectivity features, a feature bank configured to store connectivity features, and modeling backbones configured to analyze connectivity features using machine learning techniques.

The invention discloses a brain signal analysis method and system based on spatial-temporal feature enhancement, and relates to the technical field of electroencephalogram data processing.The method includes the following steps that fMRI data are collected and preprocessed; performing time sequence data enhancement on the processed data through a CRF model and an MRF model, and performing comparative learning in a potential space; then, based on manifold learning attention mechanism fusion, function connection of a weighted enhanced time sequence is constructed, and enhanced features are obtained and used for constructing a brain network graph. According to the method, the training weights of the CRF model and the MRF model are extracted to enhance the fMRI time sequence data, so that the complementary advantages of the CRF model in time modeling and the MRF model in space processing are utilized. The method is superior to an fMRI processing analysis method in the prior art, reliable joint space-time modeling is achieved, and a new view angle is provided for fMRI analysis.

The invention provides a personalized rehabilitation path making method and system based on amygdala kernel function evaluation. The method comprises the following steps: firstly, acquiring multi-modal neural function data of an individual; secondly, determining an amygdala function bias mode based on the amygdala subregion function connection data; then carrying out collaborative analysis on the amygdaloid nucleus function bias mode and preset target individual behavior characterization data; and finally, generating a personalized rehabilitation path according to a collaborative analysis result obtained by analysis. According to the technical scheme provided by the invention, the subregion-level accurate evaluation of the amygdala kernel function is realized by integrating the multi-modal neural function data and the individual behavior characterization data, and a highly individualized rehabilitation path in which neural activity and rehabilitation training are precisely coupled is generated accordingly, so that the pertinence and effect of intervention are effectively improved.