491 results about "Brain network" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

The invention discloses a trajectory portrait generation method and system based on artificial intelligence, and the method comprises the steps: generating a structured data flow graph according to a dynamic access log, a data operation behavior record and an authority change event of an enterprise heterogeneous data source; outputting high-order semantic feature representation of data flow by using a space-time diagram neural network based on the structured data flow map; according to the high-order semantic feature representation, multi-source semantic features are fused through a cross-modal attention mechanism, and a global embedding vector of the data track is generated; and based on the global embedded vector, simulating a path evolution process of data flow by adopting a dynamic brain network evolution model, predicting a potential abnormal diffusion path through a nonlinear kinetic equation, and generating a risk-aware trajectory portrait. By utilizing the embodiment of the invention, the expression capability of the track portrait can be improved, and an advanced technical basis is provided for enterprise safety management, operation optimization and decision support.

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 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 present invention relates to the technical field of medical data processing. Disclosed are an electroencephalogram-based cognitive decline risk assessment method and system. In the present invention, a plurality of electroencephalogram representations in electroencephalogram data of a target group and behavioral data of the target group are used to perform training to obtain a first cognitive decline risk assessment model, a second cognitive decline risk assessment model and a third cognitive decline risk assessment model, wherein the first cognitive decline risk assessment model mainly includes spectral information, information included in the second cognitive decline risk assessment model is mainly connection information of a brain network, and the third cognitive decline risk assessment model mainly includes neural spatio-temporal dynamics information. Thus, doctors can be assisted in performing efficient and accurate cognitive decline risk assessment on subjects to be assessed, and patients can be classified on the basis of the cognitive decline risk types of the patients, thereby helping the doctors to quickly formulate rational diagnosis and treatment strategies and improving the working efficiency and quality.

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 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 relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a Parkinson subtype diagnosis model based on balance multi-mode brain network fusion, which is characterized in that functional magnetic resonance data and diffusion tensor imaging data are preprocessed respectively to obtain fMRI signals and a structural brain network, and then a functional brain network is constructed by using a Pearson's correlation coefficient; thresholding the functional brain network and the structural brain network, respectively extracting functional and structural topological features in the functional brain network and the structural brain network, and performing modal alignment on the functional brain network and the structural brain network; then fusing multi-modal brain network features, and splicing topological features and multi-modal fusion features of functions and structures so as to reserve modal specific information and multi-modal fusion information; secondly, the spliced features are added to an adaptive weight learning module, so that contribution degrees of different modes are dynamically adjusted, weights are multiplied by the two features respectively, so that balanced multi-mode features of each subject are obtained, and the balanced multi-mode fusion features are sent to a multi-layer perceptron for classification.

The invention relates to the technical field of intelligent robots, and provides a cerebellum model and cross-modal attention fused control device, which comprises a three-layer cooperative control framework: a high-level strategy network for receiving global state data, outputting a macroscopic action element instruction and guiding the overall direction of task completion; the middle-layer cerebellar network is used for fusing multi-modal ontology sensing data in real time and outputting compensation torque; and the bottom layer actuator is used for controlling and executing joint torque output through a feed-forward PID. According to the invention, high-level action errors can be corrected online, and the dynamic anti-interference capability is remarkably improved; delay of torque compensation is reduced, and disturbances such as ground slipping and load sudden change are effectively handled; the strategy drift problem in traditional end-to-end training is solved.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention discloses an epilepsy region positioning method and system based on brain power supply imaging and a dynamic brain network, and the method comprises the steps: obtaining T1 weighted magnetic resonance imaging data of a user, and constructing an individual three-dimensional head model through a boundary element method; acquiring scalp electroencephalogram data of a user, and preprocessing the scalp electroencephalogram data; based on an individual three-dimensional head model, performing inverse problem solving on the preprocessed scalp electroencephalogram data by using a standardized low-resolution brain power source imaging algorithm to obtain source current density signals of 68 brain regions; decomposing into six frequency bands, calculating the power spectrum density of each brain region and carrying out normalization processing, and screening effective frequency bands; based on the source current density signals of the 68 brain regions of the effective frequency band, information flow directions and intensities of different brain regions are calculated by adopting a directional transfer function method, a directional transfer function matrix of the effective frequency band is formed, and a directed brain network is constructed; and calculating a graph theory index and / or an epilepsy index of each brain region, carrying out maximum value normalization analysis, and determining an epilepsy region positioning result.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention discloses a cross-individual EEG emotion recognition method based on course learning and multi-source domain adaptation, and the method comprises the steps: constructing a multi-modal feature extraction network, and integrating feature extraction modules of time sequence, frequency domain, brain network connectivity and the like, so as to fully mine and fuse multi-dimensional emotion related information. Aiming at challenges of large individual difference, inconsistent data distribution and the like in cross-subject emotion recognition, a multi-source domain adaptation mechanism is introduced, and the discrimination ability and migration performance of a model on a target domain are improved by effectively aligning feature distribution of a source domain and a target domain. Besides, in order to relieve the problems of unstable convergence and local optimization caused by difficult samples in the initial training stage of the model, a course learning strategy is introduced, target domain data is guided to participate in training from shallow to deep according to the sample difficulty, and therefore the convergence, generalization ability and robustness of the model are remarkably enhanced, and the training efficiency is improved. And finally, the overall performance of cross-subject EEG emotion recognition is effectively improved.

The invention discloses a thinking cellular system construction method for simulating spatio-temporal evolution of industrial chain nodes, and relates to the technical field of brain network construction. Comprising the steps of obtaining a thinking cell vector space; obtaining a thinking cell state; obtaining an adjacent topological relation; establishing a logic evolution dynamic potential function; constructing a thinking cellular system according to the thinking cellular vector space, the thinking cellular state, the adjacency topological relation and the logic evolution dynamics potential function; and identifying the observed state and the hidden state of the industrial chain key node based on the thinking cellular system to obtain an identification result. According to the method, dynamic simulation is performed on spatio-temporal evolution of the industrial chain key node, the evolution path of the industrial chain key node state is explicitly expressed, the evolution rule is adjusted in real time to cope with environmental disturbance, adaptive identification and risk prediction of the industrial chain key node state are realized, and the external risk resistance of the industrial chain is 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 an attention deficit hyperactivity disorder subtype recognition method based on brain network topology hub deviation, relates to the technical field of medical diagnosis, and has the technical key points that a norm model of a brain structure form similarity network is constructed based on multi-center big data; quantifying a brain network topology hub index as a target phenotype of the norm model; then, performing semi-supervised clustering analysis by utilizing the individual deviation phenotype of the topological hub index so as to divide different biological subtypes and reveal unique clinical and biological characteristics of the biological subtypes; finally, strict cross validation is carried out in an external independent queue, and it is ensured that the potential biological subtypes recognized by the typing model have good generalization and clinical effectiveness.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system and method based on the non-invasive multi-modal neural image.Through a multi-modal brain network fusion graph neural network framework guided by graph reconstruction, a self-supervised normal form is used for fully utilizing MRI data, the limitation of rare PET data under a supervision strategy is broken through, and and reliable pathological characterization embedding is generated. Meanwhile, different from conventional brain network modeling which only constructs node features, the method constructs a bimodal brain network which integrates semantic and topological information according to gray matter function signals and white matter fiber structure connection, and designs a node-edge bidirectional encoder, so that the representation capability of the brain network is remarkably improved.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The embodiment of the invention relates to the technical field of fatigue driving intervention, and particularly discloses a method and a system for identifying the influence of music intervention on a brain network under driving fatigue. According to the embodiment of the invention, through grouping experiment planning, dynamic simulation is carried out according to experiment planning data, and fatigue monitoring data is collected; eEG collection and fNIRS collection are carried out, and EEG collection data and fNIRS collection data are obtained; constructing a weighted brain network diagram, and performing time sequence synchronization on the weighted brain network diagram and the fatigue monitoring data to generate time sequence synchronization data; and performing grouping and intra-group comparative analysis on the time sequence synchronization data, identifying music intervention influence, and performing scene application recommendation. Grouping experiment planning and dynamic simulation can be carried out, a weighted brain network graph is constructed through EEG collection and fNIRS collection, time sequence synchronization is carried out on fatigue monitoring data, grouping and intra-group comparative analysis are carried out, the music intervention influence is recognized, and therefore the influence of music intervention on the brain network under driving fatigue is recognized.

The invention provides an individualized TMS target positioning method and system for spastic cerebral palsy children, and aims to realize TMS precise nerve regulation and control for different types of SCP children. According to the method, the structural features of the healthy child are obtained, and after image processing, accurate division of the SCP child brain region is achieved, the defects of a current brain injury child brain region spectrogram are overcome, and a basis is provided for seed point selection of different SCP symptoms; multi-modal image parameter features are further scanned, whole-brain voxel correlation analysis is performed based on seed points to construct functional connection diagrams, the crossed and overlapped functional connection diagrams are searched to serve as a liability focus network of different symptoms, a voxel set close to the scalp surface serves as a TMS target, and SCP child treatment targets of different clinical symptoms are accurately positioned. Based on a focus network mapping technology, functional connection characteristics related to different symptoms or symptom complexes are better positioned, a specific brain network is identified, and an accurate intervention target is provided for TMS treatment.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses an anxiety state assessment method, system and device and a medium, and relates to the technical field of electroencephalogram signal assessment. An observed value of the electroencephalogram data sequence at a set moment is extracted, and a time-varying linear coupling relation between the electroencephalogram data sequence and the observed value of the electroencephalogram data sequence is established based on a multivariate adaptive autoregression model; a conversion relation function between states corresponding to the multivariate signals is obtained through a time-varying linear coupling relation between the electroencephalogram data sequence and an observed value of the electroencephalogram data sequence, the conversion relation function is solved to obtain an ATDF value, and a dynamic causal brain network sequence is estimated through the ATDF value; according to the dynamic causal brain network sequence, electroencephalogram network time sequence features are extracted, and then the clinical anxiety state score of the testee is estimated; according to the method, the response to the brain is analyzed from the two dimensions of time and space, and the accuracy of the evaluation result of the clinical anxiety state of the testee is improved.

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 relates to the technical field of transcranial direct current stimulation, and particularly discloses a transcranial direct current stimulation intervention system which analyzes a brain structure network and a brain function network marked as effective before and after treatment by using a data processing and image analysis algorithm based on a machine learning and artificial intelligence technology. According to the method, brain structure-function network features before treatment are captured, and brain network state features of a new individual are reflected by performing feature extraction and splicing on a brain structure network and a brain function network of the new individual when brain positive reaction possibility identification and potential effectiveness detection are actually performed on the new individual. And taking the brain network state features of the new individual as an index to query feature information with similar response from a brain network state database with effective intervention, so as to identify the possibility of positive reaction of the brain of the new individual, thereby predicting whether the classical tDcs intervention normal form is effective to a patient object to be evaluated or not.