557 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 multi-modal dynamic structure-function multilayer brain network calculation method and terminal, and the method comprises the steps: obtaining a multi-modal brain image, and carrying out the preprocessing of the multi-modal brain image, and obtaining a target multi-modal brain image; constructing a dynamic functional brain network, a structural brain network and a grey matter morphological similar brain network according to the target multi-modal brain image; and calculating according to the dynamic functional brain network, the structural brain network and the grey matter morphological similar brain network through a network topology similarity principle to obtain structural-functional coupling. According to the method, after MRI data is preprocessed, a morphological brain network is obtained through sMRI, a functional connection brain network is obtained through fMRI, a structural connection brain network is obtained through DTI, a multi-layer brain network coupling index is calculated, while structural and functional connection is considered, morphological information is also considered, then exploration of the model on the brain structure and function is improved, and the method has the advantages of being high in practicability and the like. And more comprehensive and accurate brain structure and function information can be provided.

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 invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

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.

Disclosed is an EEG recognition method for a natural hand movement based on a time-domain and frequency-domain multi-layer brain network, including: (1) acquiring a multi-channel EEG signal of the natural hand movement; (2) preprocessing the multi-channel EEG signal, and extracting a δ wave, a θ wave, a α wave, a β wave, and a γ wave at each time point; (3) constructing a time-domain multi-layer brain network using a wSAR model; (4) calculating the frequency-domain multi-layer brain network using the phase-amplitude coupling; (5) combining the time-domain multi-layer brain network and the frequency-domain multi-layer brain network, and performing standardization; and (6) calculating metrics of the time-domain and frequency-domain multi-layer brain network and a super-adjacency matrix of a decomposed time-domain and frequency-domain multi-layer brain network, inputting the same to a two-layer graph convolutional network (GCN), and fusing manual, shallow, and deep features for analysis.

The invention provides an Alzheimer's disease diagnosis method based on multi-modal brain network feature fusion, which is based on a multi-modal brain image, a neural network and an attention mechanism, and has the main innovation points that: 1) effective feature combination of three types of multi-modal data of MRI, DTI and fMRI can reflect all feature information of a patient sample; and 2) aiming at different image data, a targeted feature extractor is used, the three-dimensional convolution is more suitable for feature learning of a three-dimensional MRI whole brain image, the Mama model is more suitable for learning of fMRI time sequence features, and the graph convolution network is more suitable for learning of DTI fiber number structure brain network features. And 3) the innovative mixed attention mechanism is fused with three types of multi-modal features, position coding and a Transform structure are used at the same time, the mixing effect and the classification accuracy of the multi-modal features are improved, and compared with a convolution and full connection layer structure, richer information can be learned.

The invention discloses a functional connection and dynamic model-based anatomical connection prediction method, which comprises the following steps of: firstly, constructing a neurodynamic model, taking a random matrix as initial input, combining functional connection data and the neurodynamic model, and generating a coupling matrix which is more in line with neurodynamic characteristics by iteratively optimizing the random matrix; and the coupling matrix is used for predicting anatomical connection and replacing anatomical connection data to construct a high-precision brain model. According to the method, the initial random matrix is iteratively optimized, so that the generated coupling matrix gradually approaches real anatomical connection, non-intrusive anatomical connection prediction is realized, dependence on intrusive structure connection data is remarkably reduced, and compared with an existing neurodynamics modeling method depending on dMRI data, the method has the advantages that the modeling efficiency is improved, and the modeling cost is reduced. The used DMF model achieves higher fitting degree in structure and function, has higher flexibility and adaptability, is beneficial to revealing deep connection between functional connection and anatomical connection, and provides more efficient technical support for brain network research.

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.

Disclosed in the present invention is a time-frequency multi-layer brain network-based natural hand movement EEG recognition method, comprising: (1) acquiring a multi-channel EEG signal of a natural hand movement; (2) pre-processing the multi-channel EEG signal to extract a δ wave, a θ wave, an α wave, a β wave, and a γ wave at each time point; (3) constructing a time domain multi-layer brain network by using a wSAR model; (4) calculating a frequency domain multi-layer brain network by using cross-frequency coupling (CFC); (5) combining and standardizing the time domain and frequency domain multi-layer brain network; and (6) calculating a multi-layer network metric of the multi-layer brain network and a decomposed super-adjacency matrix, and inputting same to a double-layer graph convolutional network (GCN) to fuse artificial, shallow, and deep features for classification. According to the present invention, a new time-frequency fusion multi-layer brain network is proposed by using the wSAR model and the CFC. In addition, feature information of the multi-layer brain network is comprehensively extracted by using two layers of GCNs, so that the neurophysiological mechanism of brain can be revealed and the decoding precision of the natural action EEG can be improved.

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 relates to a closed-loop feedback transcranial alternating current stimulation system and method. The closed-loop feedback transcranial alternating current stimulation system comprises a phase stimulation system used for generating a plurality of electrical stimulation signals and stimulating different brain areas in the brain based on the electrical stimulation signals; a preset phase difference exists between the electrical stimulation signals; the electroencephalogram acquisition system is used for acquiring electroencephalogram signals of the brain after the phase stimulation system stimulates the brain for a preset time; and the evaluation feedback system is connected with the phase stimulation system and the electroencephalogram acquisition system and is used for evaluating the electroencephalogram signals acquired by the electroencephalogram acquisition system and adjusting the electrical stimulation signals based on an evaluation result. According to the closed-loop feedback transcranial alternating current stimulation system, the waveform signal can be flexibly adjusted based on the real-time oscillation signal of the brain, and the problems that in the prior art, the shock wave form is single, individual differences cannot be accurately matched, and dynamic brain network state changes cannot be adapted are solved.

The invention discloses a cognitive improvement method and system based on multi-modal data. The cognition improvement method comprises the following steps: acquiring first multi-modal data of a patient before treatment; carrying out amyloid protein load grouping on the patients on the basis of amyloid protein loads in the brains of the patients so as to obtain corresponding initial cognitive training schemes; performing cognitive training based on the initial cognitive training scheme during the treatment period of the patient, and collecting second multi-modal data during the treatment period; based on the multi-modal data before and after the treatment of the patient, acquiring multi-modal change data of the patient in combination with the brain network group atlas; and inputting the multi-modal change data of the patient into a preset model to output a cognition improvement scheme for improving the cognition index of the patient to the greatest extent, and pushing the cognition improvement scheme to the patient for cognition training. According to the method, by integrating A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, the neuroplasticity change of a patient is evaluated in real time, so that a personalized cognition improvement scheme is dynamically generated.

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 discloses a functional connection network analysis method based on a group sparse attention converter. The method mainly comprises the following steps: preprocessing brain functional magnetic resonance imaging data output by a functional magnetic resonance instrument; dividing the preprocessed brain function image into a plurality of brain regions according to a standard; based on Pearson correlation and group sparse characteristics, calculating a dependency relationship between brain regions, and respectively constructing an initialized brain network and a guidance network; taking the initialized brain network as the input of a group sparse attention converter, and carrying out learning under the assistance of a guidance network so as to learn a brain network with a group similar topological structure; and subsequent classification is carried out. The method integrates priori knowledge of a population level, allows more comprehensive modeling across multiple brain networks, is designed for end-to-end adaptive learning, dynamically updates the brain networks through back propagation, and significantly improves the construction and analysis performance of the brain networks.

The invention belongs to the technical field of magnetic nanoparticle imaging, particularly relates to an MPI magnetic particle imaging method and system for early detection of an AD patient, and aims to complete MPI time sequence imaging by using a diffusion model and solve the problem that the feature dimension of an input signal in the diffusion model is not matched with the resolution of an expected generated image. The method comprises the following steps: constructing a generative network model, and performing staged training; mPI time sequence one-dimensional signals are collected, and image reconstruction is carried out based on the generative network model to obtain MPI time sequence images; carrying out registration and dividing a brain region; and constructing a functional connection diagram and a functional connection matrix by using the RV coefficient, and drawing the brain network. According to the method, by constructing the Seq-VAE / Regression / Sig-LDM step-by-step network and performing staged training, free input of a signal sequence of a fixed feature dimension is realized, the image sequence reconstruction effect is improved, a default mode network is drawn based on the reconstructed image sequence, and accurate data is provided for subsequent application.

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 relates to the technical field of image processing, and provides a brain network analysis method and system based on adaptive graph collaborative contrast learning. The method comprises the following steps: dividing a brain into a plurality of brain regions, extracting an average time sequence of each brain region, and calculating a Pearson's correlation coefficient between every two brain regions; according to the Pearson's correlation coefficient, selecting edges meeting requirements, and constructing a brain map by taking the brain region as a node; performing learnable attribute augmentation on each brain graph by adopting a graph attribute augmentation device to obtain two attribute augmentation views and an attribute augmentation probability matrix; a graph structure augmentation device is adopted to carry out learnable structure augmentation on each brain graph, and two structure augmentation views and a structure augmentation probability matrix are obtained; training a feature extractor, a graph attribute augmentor, a graph structure augmentor and a classifier by utilizing graph collaborative contrast learning; based on the brain map, a trained feature extractor is adopted to extract brain map features, and then a trained classifier is adopted to carry out brain disease classification on the brain map features.

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.