629 results about "Brain function" patented technology

The invention discloses a tumor space occupying brain function partition nerve influence image segmentation method based on a deep learning model, and particularly relates to the technical field of artificial intelligence and medical cross. Comprising the following collaborative operation steps: multi-modal data fusion acquisition, tumor edge feature enhancement, anatomy-functional feature collaborative modeling, occupation deformation compensation, adversarial boundary optimization, dynamic loss regulation and control, cascade segmentation strategy, clinical interaction verification, and construction of a U-Net + + based improved three-dimensional feature fusion network. Utilizing a multi-scale cavity convolution module to extract edge gradient features of the tumor infiltration area in a layered manner; in order to solve the problem of multi-modal fusion and dynamic adaptation mechanism deficiency in the prior art, a three-dimensional cross-modal fusion network of a channel competition mechanism is constructed, multi-modal key features are dynamically screened through spatial pyramid pooling and soft attention weight, and a tumor volume self-adaptive dynamic loss function is combined, so that multi-modal fusion and dynamic adaptation are realized. The problems of feature confusion and poor generalization of a traditional method are solved.

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

The 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 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.

A user workload state processing method and apparatus based on multimodal data, and a device. The method comprises: acquiring state information of a first user; determining workload information of the first user on the basis of the acquired state information, wherein the state information comprises at least two of the following information: physiological information, eye movement information, electroencephalogram information, brain function imaging information, motion capture information, spatiotemporal acquisition information, behavior acquisition information, and facial expression and state information; and feeding back the workload information to a first management account, and formulating a training scheme matched with the workload information. According to the embodiments of the present application, workload of users can be identified on the basis of multimodal data of users.

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 provides a brain function evaluation method and system based on fNIRS and eye movement feature fusion, and the method comprises the steps: testing a to-be-tested person under a pre-constructed social scene normal form, and obtaining test data which comprises functional near infrared spectrum data and eye movement data; extracting time dynamic characteristics of brain function activation and brain function network space organization mode characteristics from the functional near infrared spectrum data, and constructing a near infrared brain function characteristic set; for a near-infrared brain function feature set corresponding to the functional near-infrared spectrum data and an eye movement space-time feature set corresponding to the eye movement data, constructing features in each near-infrared brain function feature set and features in the eye movement space-time feature set into feature pairs, and calculating a correlation coefficient of each feature pair, feature fusion and screening are realized based on correlation coefficients; and inputting the screened features into a classification evaluation model to obtain the severity of the behavioral dysfunction and a brain function analysis value.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

The invention provides a transcranial deep electrical stimulation closed-loop regulation and control system and method based on fNIRS dynamic guidance. The method comprises the steps that a transcranial alternating current electrical stimulation module applies an electrical signal to a target deep brain region according to preset parameters to carry out electrical stimulation regulation and control; the synchronous triggering module is used for realizing millisecond-level synchronization of a stimulation task and near-infrared brain function data acquisition; the brain function monitoring module analyzes the concentration change of cortex oxyhemoglobin in real time through the multi-channel head cap; the electrical stimulation-response coupling mapping module positions a significant activation channel by adopting statistical test, and establishes an oxyhemoglobin concentration variable and electrical stimulation current amplitude quantitative model; and the dynamic parameter adjusting module optimizes the output current in real time. Personalized closed-loop regulation and control of non-invasive deep brain stimulation can be achieved, cerebral cortex blood oxygen signals are monitored in real time through near-infrared brain function imaging, stimulation parameters are dynamically adjusted, and the accuracy and individualization level of deep brain stimulation are remarkably 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 discloses a technology for targeted stimulation of a specific brain region and application of the technology in regulation and control of brain functions, and belongs to the field of animal cognitive behavior research. Techniques for targeted stimulation of specific brain regions include insulated stimulation electrodes that can generate AC voltage stimulation and a stimulation device for adjusting the AC voltage intensity and frequency. By applying the technology (ntTMS) for targeted stimulation of the specific brain region, an obvious magnetic stimulation effect can be achieved in the center of the brain region, cerebral neurons of a target region are activated, social and cognitive behaviors of animals are effectively regulated and controlled, and no obvious side effect is brought to experimental animals.

The invention is applicable to the technical field of brain discipline, and provides a brain function connection modeling and coding method, device and equipment and a storage medium, the method comprises the following steps: constructing a first brain function connection graph according to rs-fMRI data, performing graph enhancement processing on the first brain function connection graph by adopting a preset adaptive graph enhancement strategy to obtain a second brain function connection graph, performing feature coding on the second brain function connection graph by adopting a preset topological attention coding strategy to obtain corresponding graph features, and performing feature projection on the graph features by adopting a preset feature projection strategy to obtain brain function discrimination features for brain function connection classification, therefore, the generated brain function discrimination features can accurately identify subtle differences of different types of brain function connections, and high-accuracy discrimination can be realized whether normal and abnormal brain function states or brain connection modes corresponding to different diseases, so that powerful support is provided for clinical aid decision making, and the accuracy of brain function discrimination is improved. And the accuracy and scientificity of brain disease diagnosis and treatment can be improved.

The invention relates to the technical field of medical instruments, in particular to a sleep monitoring system based on a non-invasive brain oxygen saturation monitor, which comprises a monitoring system and is used for synchronously and dynamically monitoring local brain oxygen in real time, and the monitoring system comprises a non-invasive brain oxygen dynamic monitoring unit used for monitoring the brain blood oxygen saturation of a patient in real time by adopting a near infrared spectrum technology; the multi-parameter synchronous analysis unit is used for integrating the brain oxygen data, the electroencephalogram signals, the cerebral blood flow data and the respiratory event indexes; the intelligent risk assessment engine analyzes a brain oxygen change rule based on a deep learning algorithm, and predicts a brain function impairment risk caused by sleep breathing disorder; in the intelligent risk assessment engine, on the basis of a space-time convolutional neural network, brain oxygen time sequence data and spatial distribution characteristics are combined, and a brain hypoxia mode of the OSA patient is recognized; the method has the characteristic of realizing early warning of the brain injury related to sleep breathing disorder through noninvasive brain oxygen monitoring, multi-parameter fusion analysis and intelligent risk assessment.

The invention discloses a transcranial electrical stimulation physiotherapy system based on EEG-fNIRS feedback. The transcranial electrical stimulation physiotherapy system comprises an EEG-fNIRS signal acquisition and processing module, a relaxation degree evaluation module, a transcranial electrical stimulation control module and a transcranial electrical stimulation execution module. The transcranial electrical stimulation physiotherapy system provided by the invention can accurately extract spatial and temporal characteristics related to relaxation degree, constructs a personalized brain function network model, accurately locates stimulation sites and neural activity modes based on the characteristic that EEG and fNIRS data complement each other in terms of advantages in terms of time resolution and spatial resolution, and improves the rehabilitation effect of the brain function network model on the basis of the characteristic that the advantages of the EEG and fNIRS data complement each other in terms of time resolution and spatial resolution. The method realizes personalized dynamic adjustment of the relaxation degree, and is suitable for multiple fields of education, medical treatment, rehabilitation and the like.

Disclosed are devices, systems and methods for acquiring, analyzing, and utilizing neurofeedback to promote brain coherence. Neurofeedback is a form of biofeedback that allows an individual to regulate his / her brain activity by providing a visual metaphor of brain function, thereby making it accessible for manipulation. In some embodiments of the present technology, a system includes a brain signal detection device wearable by a subject and a computer device including a display and a brain-computer interface (BCI) configured to monitor brain signals and display visual, auditory, and / or tactile stimuli to the subject according to a neurofeedback threshold-based protocol to deliver brain signal coherence between the left and right hemispheres of a subject's brain.

The present invention discloses a technology for measuring brain function suitable for an infant or a moving subject who cannot be controlled easily during measurement. One embodiment of the present disclosure pertains to an ultrasound device that comprises: multiple ultrasound probes that receive / transmit ultrasound signals from / to multiple brain areas via multiple head regions and are disposed corresponding to the respective head regions; a control unit that controls the respective ultrasound probes; and an image processing unit that generates brain function network information calculated from blood flow states among the respective brain areas based on measurement results acquired from the respective ultrasound probes.

The invention discloses a depression brain function connection data analysis method based on multi-map fusion, is applied to the field of deep learning and nerve imaging, and aims to solve the problem that depression brain function connection cannot be represented accurately and effectively in the prior art. According to the method, brain region level blood oxygen concentration dependency signals are obtained from resting state functional magnetic resonance imaging data based on a plurality of brain maps divided according to different scales or functions, and then a functional connection matrix of each map is obtained through calculation based on a correlation algorithm; and then abstracting the functional connection matrix into graph structure data, carrying out feature extraction by using a multi-scale graph convolutional layer, and finally fusing and analyzing multi-map features by using two different strategies of multi-head cross attention fusion and adaptive map weight learning, so as to execute downstream tasks such as diagnosis or biomarker positioning. According to the method, multi-map information of functional magnetic resonance imaging is well integrated, the reliability of a depression brain function connection analysis result is improved, and an objective reference is favorably provided for clinic.

The invention belongs to the field of electroencephalogram signal classification detection, and particularly relates to a neuropsychiatric disease electroencephalogram diagnosis method based on iteration polar coordinate attention, which comprises the following steps: acquiring a multi-channel EEG (electroencephalogram) signal and preprocessing the multi-channel EEG signal; inputting the EEG preprocessing signal into a pre-trained improved LaBraM model to obtain a plurality of node features; calculating a Pearson's correlation coefficient matrix and a cosine similarity matrix according to the EEG preprocessing signal, and then constructing a brain function fusion connection matrix as an adjacent matrix through an iteration polar coordinate attention mechanism; according to the method, the large-scale pre-trained EEG model and polar coordinate attention are used for brain graph structure construction for the first time, and collaborative optimization of time feature generalization and space structure modeling capacity is achieved.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

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.

A driver assistance device is configured to calculate a numerical value indicating a level of a cognitive function of the driver based on information detected by detecting at least one of driving behaviors of a vehicle by a driver, biological information during driving, and a behavior of the vehicle; analyze the numerical value as cognitive function characteristics related to one or more different brain functions; store, in time series, the numerical value for the same driver and an analysis result of analyzing the numerical value; calculate degrees of influence of a plurality of variation factors that cause deterioration in the cognitive function of the driver and estimate a main factor, based on a stored content resulting from storing, in time series, the numerical value and the analysis result; and assist the driver based on an estimation result by estimating the main factor, or information corresponding to the estimation result.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention relates to the technical field of medical image processing, and discloses a brain network image analysis method, system and device for a glaucoma patient and a medium, and the method comprises the steps: carrying out the brain region scanning of the glaucoma patient, and obtaining a high-resolution structure image, a resting state function image and a diffusion tensor image; pre-processing the resting state function image and the diffusion tensor image, and respectively registering the resting state function image and the diffusion tensor image with the high-resolution structure image to obtain a registered resting state function image and a registered diffusion tensor image; performing brain region division on a glaucoma patient, wherein each region is used as a node; on the basis of registration resting state functional imaging, Pearson's correlation coefficients between the nodes are calculated to construct a whole-brain functional network; on the basis of registration diffusion tensor imaging, the number of fiber bundle connections between nodes is calculated to construct a whole-brain structure network; and carrying out coupling analysis on the whole brain function network and the whole brain structure network to obtain an F-S coupling coefficient. The method is helpful for deeply researching the influence of glaucoma on the brain network.

The invention relates to a brain fatigue real-time evaluation and prediction method based on a multi-modal signal and deep learning, and solves the problem that the existing brain fatigue monitoring technology mostly depends on static feature analysis of a single physiological signal. Firstly, EEG dynamic brain function network features and EEG time-frequency domain features are fused, and feature fusion driven by time-frequency-space multi-domain EEG features is achieved; furthermore, on the basis of weighted K-means clustering, PPG signals are automatically associated with a fatigue stage, the fatigue level is calibrated with the heart rate center value, subjective labeling is not needed, and objective re-calibration of the brain fatigue degree is achieved; and finally, realizing real-time evaluation and prediction of the brain fatigue degree based on the time sequence deep convolutional network model. The brain fatigue degree can be evaluated and predicted in real time with high precision, an early warning signal is sent to an operator with high brain fatigue degree, technical support is provided for brain fatigue real-time monitoring and warning, and the rate of misoperation accidents caused by brain fatigue is further reduced.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention provides a postoperative brain function state evaluation method and system based on multi-modal fusion, and relates to the technical field of postoperative brain function state evaluation.The method comprises the steps that multi-source original data of a postoperative patient is obtained, standardized multi-source data is obtained, and a postoperative immediate brain function baseline is established; a multi-modal feature subset is formed, and then the postoperative-preoperative function offset is obtained; generating a fusion feature vector and a confidentiality suppression index through fusion processing, and obtaining the confidentiality suppression index; a brain function state preliminary classification result and intervened newborn brain response characteristics are output through classification processing and intervention processing, and sound-light-electricity closed-loop intervention is driven; and finally, obtaining a postoperative brain function state evaluation result, and carrying out iterative calculation again until a final evaluation result which is consistent with clinical diagnosis and passes baseline matching is obtained. The method has the advantages that rapid and accurate evaluation and individualized intervention of the postoperative brain function state are achieved, and then the efficiency and quality of postoperative brain function management are improved.

The invention belongs to the technical field of brain function monitoring, and discloses a three-in-one non-invasive brain function monitoring system and method, a medium, equipment and application, and the monitoring method comprises the following steps: monitoring brain oxygen saturation of a critical patient; carrying out transcranial Doppler ultrasonic measurement on intracranial blood vessel blood flow velocity and ultrasonic measurement on the inner diameter of the optic nerve sheath; monitoring a quantitative electroencephalogram of the patient; various brain function monitoring series indexes and continuous changes of the indexes along with disease evolution are obtained, the pathogenic mechanism and pathogenesis of the indexes are found in time, and primary diseases and related complications of the primary diseases are treated. According to the invention, the brain oxygen saturation can avoid too low brain oxygen or low brain oxygen for a long time and too high brain oxygen due to oxygen utilization disorder; cerebral blood flow can be monitored through a transcranial Doppler (TCD) technology to find an optimal craniocerebral perfusion pressure (CPP), and a cerebral blood flow self-adjusting function is enabled to be in an optimal state; the electroencephalogram can avoid early warning of over-sedation or abnormal discharge and the like. Through organic combination of the three, noninvasive, real-time and dynamic monitoring of critical patients is realized.

The invention discloses a task state brain-computer interface training system for closed-loop transcranial magnetic stimulation, and particularly relates to the field of cognitive rehabilitation assistion.The task state brain-computer interface training system comprises the steps that multi-channel electroencephalogram signals and brain function activation records are synchronously collected in the rehabilitation training process, task segments are divided, and frequency domain energy characteristics of all channels are extracted; identifying a state transition candidate segment based on the frequency band energy distribution change; the method comprises the following steps: constructing a cross-channel phase synchronization matrix, and inputting a graph convolutional neural network to generate an activation topology vector reflecting a brain region cooperation mode; the system constructs an activation state recognizer by taking a topological vector and a brain function activation label as supervision data, and realizes real-time judgment of different function states. The recognition result drives a stimulation parameter recommendation module, a preset stimulation strategy mapping table is inquired according to the recognition state, recommended stimulation parameters are dynamically generated and transmitted to a magnetic stimulation execution interface, and personalized closed-loop nerve regulation and control are achieved.

The invention discloses a magnetic resonance method for evaluating the rehabilitation curative effect of child cerebral palsy, and relates to the technical field of medical image processing. The method comprises: obtaining structural image data (T1 weighted imaging and diffusion tensor imaging) and resting state image data of each child patient; and extracting brain and tissue boundaries from the T1 weighted imaging by using a 3D-Unet segmentation model. The resting state and structure image data are preprocessed and aligned to the brain boundary, and then the data are registered to a child standard brain template; based on a child standard brain template, obtaining and normalizing various clinical indexes, splicing the clinical indexes into multi-index vector data, inputting the multi-index vector data into a pre-trained support vector machine model, and outputting clinical index differences; and determining an abnormal brain region of each child according to the difference, finally providing a multi-modal fusion index analysis report, and observing changes of brain functions and white matter integrity before and after rehabilitation.