823 results about "Brain region" patented technology

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

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

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

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

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

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

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

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

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

The invention discloses a neural disease diagnosis system based on space-time attention and dynamic field self-adaption. The method belongs to the technical field of cross-modal medical data adaptive analysis. The technical problem that a brand new system capable of simultaneously fusing multi-modal information and modeling multi-scale spatial-temporal features and having dynamic field adaptive ability is urgently needed to improve the accuracy and generalization ability of intelligent diagnosis of multi-site nerve diseases is solved. The system comprises a data preprocessing module for extracting a standardized time sequence of a brain region from fMRI time sequence data; the two-channel feature coding network module obtains global features through an attention mechanism; according to the feature fusion and classification module, a main task classifier executes a main task and predicts whether a to-be-tested person suffers from nerve diseases or not, and a domain task classifier executes a domain task and predicts a site to which the to-be-tested person belongs; and the dynamic balance training module adjusts the dynamic balance of the main task and the domain task through a dynamic balance control strategy.

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 medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

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

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

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

The invention discloses an electroencephalogram epilepsy detection method and system based on a node adaptive graph neural network, relates to a computer system based on a biological model, and provides the scheme for solving the problems of graph structure immobilization and the like in the prior art. The method comprises the following steps: an electroencephalogram signal acquisition and preprocessing step; constructing a hybrid EEG graph; optimizing a self-adaptive residual image; node specific diffusion convolution is carried out; modeling time sequence characteristics; and performing classified output. The system comprises a data acquisition module, a mixed graph construction module, a self-adaptive mapping module, a node specific convolution module, a time sequence modeling module and a classification output module. When the system runs, the steps of the method are executed, so that the electroencephalogram epilepsy detection function based on the node adaptive graph neural network is realized. The method has the technical advantages that (1) graph structure self-learning is carried out; (2) carrying out brain region personalized modeling, and strengthening region feature expression; and (3) combining space-time dependence modeling, and completely depicting the epilepsy dynamic process.

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 invention provides a method for constructing a mood disorder assessment model based on hierarchical multilevel gating, which comprises the following steps: acquiring electroencephalogram signals of a plurality of testees, labeling labels, and constructing a first training sample set; training by using the first training sample set to obtain time sequence dynamic graph network feature extraction models corresponding to the multiple electroencephalogram parameter combinations and related time sequence dynamic graph network features; constructing a second training sample set by using the first training sample set and the time sequence dynamic graph network features, and training a hierarchical multilevel gating model; and obtaining a mood disorder assessment model based on the time sequence dynamic graph network feature extraction model and the hierarchical multi-level gating model corresponding to each electroencephalogram parameter combination. According to the method, the problem that the assessment accuracy is limited due to the fact that a mood disorder assessment model in the prior art does not consider the correlation between different parameter combinations among the brain region, the frequency band and the observation time length and the task and does not perform hierarchical screening on different parameter brain networks based on the task correlation is solved.

The invention relates to the technical field of determination of nerve regulation and control precise stimulation targets, and discloses an individualized multi-frequency regulation and control stimulation system for Rett regulation and control. The individualized multi-frequency regulation and control stimulation system comprises a data acquisition module, a structure and function brain image analysis module and a multi-frequency electroencephalogram self-adaptive regulation and control module, the data acquisition module is used for acquiring resting state functional magnetic resonance imaging data rs-fMRI, structural magnetic resonance imaging data sMRI and EEG data, and the structural and functional brain image analysis module is used for identifying an abnormal brain area of an RTT patient and determining an optimal stimulation target. The multi-frequency electroencephalogram self-adaptive regulation and control module comprises two sub-modules including an electroencephalogram state recognition module and an FUS self-adaptive stimulation module, and the electroencephalogram state recognition module is used for analyzing EEG signals, extracting frequency band power change and instantaneous phase information corresponding to a target brain region and providing real-time feedback signals for self-adaptive regulation and control. According to the system, the target spot is accurately determined, and the individualized accurate stimulation target spot is determined by combining the structure and function image and the disease exposure model.

The invention discloses a method for predicting the curative effect of brain stimulation on MCI based on a multi-modal image, and belongs to the technical field of medical data process.The method comprises the steps that through a multi-modal MRI feature fusion model, structure and functional MRI indexes are synchronously integrated, feature selection and dimension reduction are conducted through comparison between independent sample t test groups and LASSO regression, and the curative effect of brain stimulation on MCI is predicted; compared with the existing method, the method has the advantages that the obvious contribution of the change of the collaborative structure image index and the functional image index to the predicted curative effect is verified through the structure-function coupling analysis, and the prediction model is constructed, so that the prediction efficiency is improved. The contribution direction and strength of each image feature to curative effect prediction are analyzed and quantified in combination with SHAP values, feature weights are automatically optimized according to SHAP value distribution, and model high-contribution feature brain regions are revealed through SHAP so as to clarify curative effect biomarkers.

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 discloses an asynchronous electroencephalogram collection system and method based on an event triggering mechanism. An input end directly receives a multi-channel electroencephalogram signal, an event triggering sensing module, an input end connected with an output end of an event feature conditioning module, a high-precision time sequence marking module and an output end controlled by the event triggering sensing module. The enabling end of the asynchronous electroencephalogram signal collecting module is connected with the output end of the event triggering sensing module, and the input end of the asynchronous electroencephalogram signal collecting module receives multichannel electroencephalogram signals. According to the asynchronous electroencephalogram collection system and method based on the event triggering mechanism, design is conducted based on the natural asynchronous activity rule of the brain, non-forced synchronous collection is achieved, and the excitation and conduction sequence of the brain interval is reflected more truly; through the hardware-level event triggering unit, event response can be accurately captured in real time, and the system is far better than a traditional synchronization system.

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

The invention 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 an electroencephalogram emotion recognition method based on multi-scale convolution and an attention mechanism. The method comprises the steps that firstly, original electroencephalogram signals are processed; secondly, extracting space and frequency features of the electroencephalogram signals by utilizing a feature pyramid network, and capturing multi-level information of local and global brain regions in a multi-scale convolution structure; a multi-scale attention aggregation module is further introduced, and brain region feature self-adaptive weighting is achieved through a parallel space and channel attention mechanism; secondly, global modeling and long-range dependence capture of time domain features are achieved through a Transform coding structure; and finally, outputting an emotion recognition result through a full-connection classifier. According to the method, the time domain, frequency domain and space domain features of the electroencephalogram signals can be extracted at the same time, efficient and accurate emotion classification is achieved, the robustness and universality of electroencephalogram emotion recognition are remarkably improved, and the method can be widely applied to the fields of intelligent human-computer interaction, mental health monitoring, emotion regulation and control and the like.

The invention relates to the technical field of medical data processing and nerve regulation and control, in particular to a method for synchronously activating post-stroke neuroplasticity through photoacoustic, which comprises the following steps of: 1, constructing a stroke specific brain region target map: fusing brain structure connection data and functional network data of a patient, generating a three-dimensional target map containing the high-metabolism semi-dark band coordinate set and the cross-hemisphere compensatory connection intersection; 2, calculating a time-space synchronization focusing parameter; 3, synchronous stimulation and dynamic regulation are executed, wherein the ultrasonic transducer and the laser source are controlled to emit time-space synchronous sound waves and light pulses; monitoring the blood perfusion variable quantity and the neurotransmitter concentration ratio of the target brain area in real time; when the blood perfusion variable quantity does not reach the expectation or the transmitter ratio is unbalanced, the stimulation intensity is dynamically adjusted, and the focusing coordinate is translated in the direction away from the infarction area. Through photoacoustic stimulation of time-space synchronization, the difference between the propagation speeds of sound waves and light waves is overcome, and accurate collaboration of neural restoration is achieved.

The invention belongs to the technical field of image processing, and relates to a deep learning framework for enhancing Alzheimer's disease classification, which comprises a convolutional backbone network for extracting multi-scale semantic features stage by stage and a convolutional feature extraction structure comprising five stages, and the convolutional feature extraction structure comprises a convolutional multilayer perceptron module; the interpretable expansion large convolution kernel convolution module is used for simultaneously capturing a local fine-grained spatial relationship and the importance of global position information and comprises a three-dimensional convolution structure used for extracting spatial enhancement features and a weight distribution mechanism used for focusing semantic information at a spatial block level; the graph attention enhancement module is used for modeling a topological dependency relationship between brain intervals and comprises a screening strategy, a multi-head attention mechanism, a graph convolution and a self-adaptive graph neural network; according to the method, the receptive field is expanded, the multi-scale features and the context information are fused, and the robustness is improved.