627 results about "Eeg data" patented technology

The invention discloses a multi-modal signal fusion emotion recognition method based on an attention mechanism. The method comprises the following steps: firstly, preprocessing physiological signal data; constructing a channel attention module for the multi-channel electroencephalogram data; respectively extracting EEG and other physiological signal features by using the EEGNet; a shared-private encoder is introduced to decouple shared features among the multiple modes and private features of each mode; and finally, providing a cross-modal cross attention fusion mechanism to realize multi-modal feature interaction and effective fusion. According to the method, multi-modal signals are combined, the limitation of a single mode is overcome, and a sharing-private feature separation mechanism and a cross-modal cross fusion mechanism are introduced, so that modal collaborative modeling and effective integration are realized, and the accuracy and generalization ability of emotion recognition are improved.

The invention discloses an emotion recognition method based on a space-time multi-scale attention convolutional neural network, and the method comprises the steps: collecting electroencephalogram data of a subject, and carrying out the preprocessing of the electroencephalogram data, so as to obtain electroencephalogram (EEG) data containing a space dimension and a time dimension; constructing a lightweight convolutional neural network comprising a double-flow spatial-temporal feature construction layer, a mixed attention mechanism layer, a high-order fusion layer and a classification layer; wherein the double-flow spatial-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the mixed attention mechanism layer combines a channel attention mechanism, a space attention mechanism and a self-attention mechanism; the high-order fusion layer is used for carrying out re-learning from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; and recognizing the EEG data by using the trained lightweight convolutional neural network to obtain an emotion recognition result of the subject. The precision and efficiency of emotion recognition driven by electroencephalogram signals are improved by constructing a lightweight model with a small number of parameters.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention relates to the technical field of music feedback, in particular to a music feedback training interface interaction method and system based on electroencephalogram data, and the method comprises the following steps: collecting electroencephalogram data of a user based on an electroencephalogram collection device, and removing environmental noise and non-target signals through signal amplification and filtering processing to obtain a user electroencephalogram data set. According to the method, the emotion recognition accuracy and time sensitivity are improved by analyzing the brain wave frequency band intensity, judging the emotion state of the user and recording the emotion change time point, and the emotion recognition accuracy and time sensitivity are improved by matching music tracks of the emotion state, combining real-time monitoring of electroencephalogram reaction and adjusting the rhythm, volume and spatial sound effect setting of music. Dynamic adjustment of music output parameters is achieved, the emotion intervention requirement is optimized, emotion improvement and user satisfaction are evaluated according to brain wave changes of the user, and the improvement effect of music intervention on the psychological state of the user can be measured.

The invention discloses an attention deficit hyperactivity disorder recognition system and recognition method, and belongs to the technical field of attention deficit hyperactivity disorder. The data processing module is used for carrying out preprocessing and feature extraction on the acquired electroencephalogram data; a multi-source feature fusion mechanism is firstly used for the extracted original feature data, and then a data enhancement strategy is applied; a multi-source fusion feedback regulation network model is constructed, wherein the model is of a CNN-GRU parallel modeling structure; the training module is used for inputting the enhanced data into a model for training, key hyper-parameters are dynamically adjusted by a performance feedback adjusting mechanism in the training process, and the performance feedback adjusting mechanism is used for dynamically adjusting key training parameters according to the performance of the verification set; and the classification module is used for classifying to-be-detected samples through the trained multi-source fusion feedback regulation network model and outputting a final recognition result. The ADHD electroencephalogram recognition method effectively improves the accuracy, robustness and generalization performance of ADHD electroencephalogram recognition.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention discloses a multi-path deep neural network-based ratchet wave superposition ripple detection method. The method comprises the following steps: 1, collecting electroencephalogram data of a real BECTS patient, preprocessing and slicing; 2, performing feature extraction and dynamic spectrum weighted fusion on the processed low-frequency and high-frequency electroencephalogram signals by using a deep neural network, and outputting a high-order feature map; 3, a multi-head attention mechanism and a coding layer are introduced, dynamic weight distribution and global dependency relationship modeling of electroencephalogram signal features are achieved, and a classification result is output; 4, dynamically processing a sample imbalance problem by adopting weighted cross entropy loss and differentiable AUC loss, and constructing and optimizing a classification model; 5, the optimized and adjusted RonS detection model is applied, and electroencephalogram RonS detection is achieved on the continuous EEG signals. According to the method, the multi-kernel causal convolution module is constructed to extract the time domain features of the ratchet waves, and the PSD weighting mechanism is combined to fuse the multi-band information, so that the detection precision and generalization ability of the RonS in the complex electroencephalogram signals are remarkably improved.

The invention belongs to the technical field of hearing testing, and relates to a self-adaptive hearing screening optimization method based on acoustic feature analysis, which comprises the following steps: generating audio data with different signal-to-noise ratios through a constructed audio library, measuring an acoustic transfer function of a user ear canal by using a probe microphone, and generating a personalized acoustic transfer function in combination with a head-related transfer function. Time domain and time frequency features are extracted by means of EEG data, and the auditory reaction ability and the spatial auditory ability of the user are quantified in combination with the spatial positioning features of the binaural simulation microphones; the method comprises the following steps: constructing a multi-modal auditory feature vector, weighting features in combination with a multi-head attention mechanism, predicting the hearing age of a user through a time sequence Transform encoder in combination with the age of the user and a standard threshold value, and evaluating hearing loss according to a prediction result; the individual difference of each user can be accurately adapted, the defect that individual difference and complex environmental noise are neglected in a traditional hearing screening method is overcome, and therefore the accuracy of a screening result is improved.

The invention provides an electroencephalogram model training method and device and a storage medium, and the method comprises the steps: segmenting a first original EEG data sample into a first original EEG fragment set, and covering a part of original EEG fragments in the first original EEG fragment set to obtain a corresponding visible EEG fragment subset and a covered EEG fragment subset; corresponding visible EEG simulation frequency domain coding features are determined through coding; extracting original signal frequency domain features, and reconstructing each visible EEG fragment according to visible EEG simulation frequency domain coding features to calculate frequency domain reconstruction loss; decoding the visible EEG simulation frequency domain coding features to predict a codebook basic class corresponding to each masked EEG fragment, and calculating classification loss by combining a codebook basic class label of each masked EEG fragment; and performing training optimization on the electroencephalogram model according to the frequency domain reconstruction loss and the classification loss. Therefore, double optimization of the frequency domain reconstruction task and the classification task of the electroencephalogram model is realized.

The invention discloses a cross-domain electroencephalogram emotion recognition method for progressive target tested sample mining, which comprises the following steps of: firstly, collecting electroencephalogram data in different emotion inducing scenes, preprocessing, extracting characteristics of different channels and frequency bands, and generating source domain and target domain data; secondly, by aligning marginal distribution and conditional distribution of a source domain and a target domain, a target domain characteristic label matrix is determined based on similarity, and a sample confidence coefficient weight matrix is obtained; secondly, measuring an error between a sample and a characteristic label by using cross entropy, and constructing a target function by combining a sample confidence coefficient weight matrix, marginal distribution and a conditional distribution alignment error; and finally, according to the target function, carrying out joint iterative optimization, and according to the target domain characteristic tag matrix after iterative optimization, judging the emotion category of the testee during electroencephalogram data acquisition. According to the method, the overall performance of the model is effectively optimized, the global optimality of the result is ensured, and more accurate electroencephalogram emotion recognition is obtained.

The invention discloses an intelligent asthenopia control method and system based on eye movement and electroencephalogram data. The method comprises the steps that eye movement time sequence data and electroencephalogram rhythm signals of a user are synchronously obtained through a multi-mode sensing unit; inputting the eye movement time sequence data and the electroencephalogram rhythm signal into a multi-modal fusion decision model, and generating a fatigue level through feature weighting based on an attention mechanism; at least one intervention mode is dynamically selected according to the asthenopia level, and the intervention modes comprise the first mode, the second mode and the third mode; in the first mode, a display interface adjusting instruction containing natural light simulation parameters is generated, and the display interface adjusting instruction is integrated with a vegetation color system compensation algorithm; and mode 2: starting a visual training program with ecological images, and generating a dynamic guide mark simulating natural motion on an interface. According to the invention, through a data-environment-physiology multi-dimensional collaborative innovative architecture, the spanning of asthenopia regulation from passive response to ecological active restoration is realized.

The invention discloses an electroencephalogram signal processing method and system based on motion artifact prediction. The method comprises the following steps: acquiring experimental electroencephalogram data when a testee executes a motion imagination task; discrete wavelet transform is carried out on experimental electroencephalogram data, and signals are decomposed into low-frequency components and high-frequency components through a low-pass filter and a high-pass filter; inputting the low-frequency component into a pre-constructed and trained ARIMA model, and predicting to obtain a linear artifact; inputting the high-frequency component into a pre-constructed and trained XGBoost regression model, and predicting to obtain a nonlinear artifact; combining the linear artifacts and the nonlinear artifacts to generate a complete artifact prediction signal; real electroencephalogram signals are separated through difference value calculation of the experimental electroencephalogram data and the artifact prediction signals. According to the method, the time sequence change of the motion artifacts is predicted by utilizing the ARIMA model, and the nonlinear artifact features are captured in combination with the XGBoost regression model, so that the motion artifacts can be effectively removed, and purer electroencephalogram signals can be recovered.

A method of enabling a user to visualize, review and analyze a patient's EEG data, pre-surgical data and post-surgical data, includes generating a unified viewing environment, wherein the unified viewing environment has at least a first view area and a second view area; importing, using the unified viewing environment, pre-surgical data and post-surgical data; acquiring EEG data of the patient; displaying, simultaneously, the EEG data in the first view area and the post-surgical data in the second view area of the unified viewing environment, wherein the EEG data is displayed as a vertical stack of a plurality of EEG traces, and wherein the post-surgical data is displayed without a need to pre-process and co-register with a three-dimensional coordinate system; and automatically associating visual representation of each of one or more electrodes and contacts in the post-surgical data with an EEG trace of the plurality of EEG traces.

The invention relates to the technical field of image processing, and particularly discloses a high-quality video reconstruction method based on electroencephalogram signals, which comprises the following steps: preprocessing electroencephalogram data, down-sampling video frames, and segmenting the electroencephalogram data by using a sliding window technology; selecting a channel with high correlation with video reconstruction; electroencephalogram time features are extracted; electroencephalogram space-time features are extracted; electroencephalogram time-frequency features are extracted; the electroencephalogram time-space-frequency features are fused; extracting the category and semantic information of the electroencephalogram; adopting a comparative learning method to align electroencephalogram-text-picture features; generating a reference video frame by using a stable diffusion model; and generating a high-quality video by using the video diffusion model. According to the method, the information of electroencephalogram features is further deeply extracted, the semantic content of the video is remarkably enhanced, meanwhile, the introduced video diffusion model gradually constructs a clear and natural picture through a step-by-step denoising process, the consistency between video frames is remarkably improved, and therefore the high-quality video rich in details is generated.

The invention relates to an instant denoising method for electroencephalogram signals of students in a classroom scene. The method comprises the following steps: based on electroencephalogram acquisition equipment and a Daisy expansion board, acquiring electroencephalogram signal data of a student through a dry electrode device; and carrying out sliding mean filtering on the electroencephalogram signal data to remove baseline drift, eliminating noise by adopting a band-pass filter to obtain preprocessed electroencephalogram signal data, and inputting the data into a lightweight neural network model to carry out channel-by-channel denoising processing to obtain a denoised electroencephalogram signal. The model comprises a multi-scale feature extraction layer, a time sequence modeling module and a residual connection structure. According to the method, through lightweight neural network design such as multi-scale feature extraction, time sequence modeling and a residual connection structure, the denoising effect is improved, the real-time performance and high efficiency of electroencephalogram denoising processing are guaranteed, and an electroencephalogram data basis with a high signal-to-noise ratio is provided for subsequent teaching quality evaluation.

The invention relates to the technical field of electroencephalogram monitoring, in particular to a non-contact electroencephalogram monitoring method and system, and the method comprises the steps: obtaining a corresponding head image of a target user at a current moment; determining a corresponding head pose of the target user at the current moment based on the head image corresponding to the current moment; based on the head pose at the current moment and the head pose corresponding to the previous moment, the pose of an electroencephalogram cap is determined, the electroencephalogram cap comprises a plurality of brain electrodes, and the brain electrodes monitor electroencephalogram data of the target user under the pose corresponding to the electroencephalogram cap. The problems that in the non-contact electroencephalogram collecting process, when the head moves, the collecting position deviates, and signals are not stable can be solved.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention discloses an electroencephalogram data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: preprocessing an original electroencephalogram signal; performing micro-state division on the preprocessed electroencephalogram signals on the five frequency bands respectively; the five frequency bands comprise a delta frequency band, a theta frequency band, an alpha frequency band, a beta frequency band and a gamma frequency band; the micro-states comprise four types of micro-states; performing feature extraction on the micro-state of the electroencephalogram signal under each frequency band; the features comprise a time feature, an occurrence frequency feature and an occupancy feature of each micro-state category, and a transition probability feature of each micro-state; and performing feature screening by using an SVM-RFE model to obtain target features. According to the technical scheme provided by the invention, the airspace information of the electroencephalogram signals in different frequency ranges is considered, the feature classification is more comprehensive, the SVM model and the RFE algorithm are combined, the performance of the model is improved, the calculation time is shortened, the calculation cost is reduced, and the electroencephalogram data recognition effect is improved on the whole.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention relates to the technical field of fatigue detection, in particular to a pilot pre-post fatigue accumulation prediction method based on a GCN-T algorithm, and the method comprises the steps: obtaining an original EEG signal of a pilot based on a Stroop fatigue stimulation experiment before the pilot is on the post; the method comprises the following steps: preprocessing an original EEG signal to extract EEG data, inputting the EEG data into a GCN-Transform model for classification, and obtaining the state of a pilot before the pilot is on duty; wherein the state comprises a normal state, general fatigue or severe fatigue; according to the method, the fatigue state of the pilot is predicted by introducing short-time fatigue stimulation and combining the EEG, and the future fatigue risk of the pilot can be evaluated under the condition that normal work tasks of the pilot are not interfered.

The invention relates to an electroencephalogram signal classification method based on multi-scale causal convolution and KAN attention. The electroencephalogram signal classification method comprises the steps of 1, EEG data collection and preprocessing; 2, EEG data enhancement; and 3, motor imagery task classification based on EEG signals. According to the method, the spatial-temporal characteristics of the EEG signals under different frequencies can be effectively extracted, the multi-scale nonlinear characteristics are efficiently fused and weighted, and the problem that nonlinear fitting of the EEG signals in a motor imagery task is insufficient is solved, so that more accurate and reliable motor imagery classification is realized.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

The invention relates to an electroencephalogram signal processing method based on spatial-temporal characteristics and graph enhanced Transform, which comprises the following steps of: (1) preprocessing electroencephalogram data, and mapping the electroencephalogram data to a hidden space to obtain a spatial-temporal characteristic shared embedded vector; (2) respectively inputting the embedded vector into a spatial domain attention branch and a time domain graph enhanced attention branch, extracting spatial-temporal characteristics of electroencephalogram, and analyzing interaction between brain intervals; (3) fusing the outputs of the two branches to form a feature vector integrating spatio-temporal information, and sending the vector into a classification head to obtain a final classification result; and (4) comparing a classification result output by the model with a real label, calculating loss, optimizing model parameters by using back propagation, and carrying out iterative training. According to the scheme, the accuracy and reliability of electroencephalogram signal detection are improved.

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 relates to the technical field of early detection of neurological diseases, and discloses an application of a portable three-dimensional eye tracker in cognitive function assessment, comprising: a portable three-dimensional eye tracker used for collecting eye movement data, including fixation time, glancing trajectory, review frequency and eye jump speed; the electroencephalogram acquisition equipment is used for acquiring electroencephalogram data, and the electroencephalogram data comprises alpha wave energy E alpha, beta wave energy E beta and theta wave energy E theta; the heart rate variability monitoring equipment is used for collecting heart rate data, and the heart rate data comprises the ratio RLF / HF of low frequency (LF) to high frequency (HF). The accuracy of cognitive function evaluation is greatly improved through multi-modal data fusion, real-time monitoring and personalized feedback are provided, early discovery of cognitive impairment is facilitated, and the device is high in portability, supports long-term monitoring and testing anytime and anywhere, and is suitable for early detection and continuous evaluation of Alzheimer's diseases.

The invention discloses an EEG visual stimulation mixed mode decoding method and system, and the method comprises the following steps: obtaining EEG data, and constructing an image-category-semantic three-state EEG data set; the mixed mode encoder learns from the EEG data to obtain corresponding image, category and semantic mode information; mapping the three-state EEG data set to a corresponding CLIP space to obtain a target mode; performing comparative learning on modal information output by the hybrid modal encoder and a target modal, and performing image retrieval and classification tasks based on the image modal information; and constructing a diffusion prior model, aligning the image, category and semantic modal information output by the hybrid modal encoder with a target modal in a CLIP space, outputting the image, category and semantic modal information subjected to diffusion prior processing, and inputting the image, category and semantic modal information subjected to diffusion prior processing into a pre-trained image generation model to perform a generation task. According to the method, multiple modes of the brain data can be effectively decoded, and the retrieval and reconstruction task effect is improved.

The invention discloses a cross-subject electroencephalogram emotion recognition method and system based on dynamic domain invariant representation decoupling and recombination, and belongs to the technical field of artificial intelligence. The invention provides a non-personalized decoupling and recombination framework for cross-subject electroencephalogram emotion recognition, and aims to separate emotion-related individual invariant features from cross-subject individual invariant features through complex electroencephalogram signal characterization obtained through dynamic decoupling, so that individual differences are eliminated while emotion classification performance is guaranteed. Specifically, the method comprises the following steps: carrying out original EEG data analysis and preprocessing by using MATLAB and Python MNE libraries; frequency spectrum and space features of EEG signals are extracted through a multi-channel frequency spectrum space self-attention mechanism module, and capture of emotional features is enhanced in combination with a self-attention mechanism and a cross-attention mechanism; a joint distribution alignment method based on a category prototype is adopted, and decoupled feature distribution is optimized, so that invariant features in subjects and invariant features among subjects have higher distinction degree in emotion classification; the optimized decoupling features are recombined through a linear network, the two features are coordinated to perform more sufficient emotion representation extraction, and emotion classification is performed through a multi-layer perceptron. According to the method, excellent cross-subject emotion recognition performance is obtained on multiple data sets, and the emotion recognition rate of the electroencephalogram signals in a cross-subject scene can be effectively improved.

The invention discloses a spatial auditory attention dynamic coupling analysis method and system based on a multilayer network, and the method comprises the following steps: collecting electroencephalogram signals of a plurality of brain regions induced by a spatial auditory attention task in a complex acoustic environment, and carrying out the electroencephalogram data preprocessing; constructing a multi-layer rhythm network and a multi-layer time-varying network; constructing a cross-time-frequency coupling super-connection network based on the multi-layer rhythm network and the multi-layer time-varying network brain configuration, and determining a core network layer of the cross-time-frequency coupling super-connection network by using interlayer correlation and interlayer conditional probability; and carrying out quantitative analysis on the network attributes of the core network layer to obtain a connection mode and a cooperative working mode between the responsible brain regions. According to the method, the four-dimensional hyperconnection network is constructed, frequency and time information is integrated, and a complex interaction mechanism of spatial auditory attention is accurately and quantitatively analyzed.

The invention discloses an auditory attention decoding method and system based on a multi-scale frequency-space attention network, and the method comprises the following steps: obtaining electroencephalogram data when a user focuses on listening to voices in different directions, dividing the electroencephalogram data through a sliding window, generating a series of decision windows, and enabling each decision window to comprise an electroencephalogram signal; a multi-scale time domain convolution module processes the input electroencephalogram signals and extracts time domain features of different frequency ranges, the frequency-space attention module converts the time domain features into a frequency-space feature map, and the frequency-space feature map is used as a frequency-space feature map; further capturing a global dependency relationship among different brain regions, and extracting frequency spectrum space information related to auditory attention; and the full connection layer classification module outputs the probability that the predicted voice direction is the left side direction or the right side direction based on the spectrum space information. According to the method, the auditory attention decoding accuracy is remarkably improved.

The invention discloses an EEG motion image classification modeling system based on self-supervised contrast learning and a modeling method thereof, and the system comprises an obtaining module which is used for obtaining EEG data; the domain self-adaptive alignment module is used for aligning EEG data of the source domain and the target domain; the data enhancement module is used for performing enhancement operation on the unlabeled source domain EEG data; the mask modeling module is used for performing mask of partial time points on the enhanced EEG data and extracting EEG feature representation through an encoder; the multi-view space-time attention module is used for processing EEG features and generating EEG feature vectors; the comparative learning module is used for realizing reconstruction of an original sequence through a reconstruction decoder and carrying out comparative learning and self-supervised pre-training; and the classification module optimizes model parameters through classification loss and applies the model parameters to target domain data to realize cross-domain classification. According to the method, the individual variability of the EEG signals can be effectively processed, and the accuracy of motion image classification and the cross-domain adaptive capacity of the model are improved.