319 results about "Motor imagery" patented technology

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention relates to a motor imagery recognition method combining EEG (electroencephalogram) and functional near infrared spectroscopy, which comprises the following steps: synchronously acquiring EEG data and functional near infrared spectroscopy signal fNIRS data under a motor imagery task through a multi-channel acquisition system, and preprocessing and enhancing the acquired two modal data. Aiming at the characteristics of the two signals, respectively extracting the time-frequency characteristics and the space-domain characteristics of the EEG and the space-domain characteristics of the fNIRS; designing a mask auto-encoder for each modal feature to carry out pre-training; and finally, carrying out feature fusion through a cross-modal cross attention mechanism in combination with parameters obtained by pre-training, and carrying out training through cross entropy loss. According to the method, the accuracy of motor imagery recognition is improved, and independent information and complementary information of EEG and fNIRS are effectively combined.

The invention provides a motor imagery classification neural network method based on multi-scale spatial-temporal feature fusion. According to the method, the EEG signal decoding precision in a motor imagery task is effectively improved by designing a multi-scale spatial-temporal feature fusion network. The method comprises the following steps: firstly, processing an original EEG signal by adopting standardized preprocessing; secondly, extracting features of the signals under different time scales through a multi-scale time sequence attention module, and enhancing a long-term time sequence dependency relationship in combination with a self-attention mechanism; thirdly, a self-adaptive channel weighting module is introduced, the weight of each electrode signal is dynamically adjusted, and representation of spatial features is optimized; and finally, fusing the spatio-temporal characteristics through a Transform module, capturing complex interaction of the spatio-temporal dependency relationship, and finally outputting a decoding result through a classifier. The method can effectively overcome the defects of an existing method in the aspects of spatial-temporal feature fusion and dependent modeling, improves the accuracy of EEG signal classification and the system robustness, and has a wide application prospect.

The invention relates to a motor imagery ability evaluation and training enhancement system and method based on electroencephalogram and a medium, and belongs to the technical field of brain-computer interfaces. The system comprises an electroencephalogram acquisition device, a processing terminal and a display device. By collecting and analyzing electroencephalogram signals of a subject, the system extracts time-domain, frequency-domain and space-domain features by using a multi-feature fusion technology, so that accurate quantitative evaluation of motor imagery ability is realized. The evaluation core index is a lateral index. A built-in self-adaptive training module dynamically adjusts training difficulty and comprises a basic mode, a middle-level mode and a high-level mode, and personalized efficient training is ensured. The method comprises pre-training guidance, data acquisition and processing in formal training, and adaptive training adjustment based on an evaluation result. By combining multi-feature fusion and an adaptive training mechanism, an efficient and personalized solution is provided for evaluation and enhancement of motor imagery ability, so that the rehabilitation training effect of a motor imagery brain-computer interface system is more effectively improved.

The invention relates to a motor imagery electroencephalogram signal classification method based on multi-scale time sequence fusion. The method comprises the following steps: S1, standardizing electroencephalogram signals before inputting a network model; s2, inputting the processed data into a multi-scale channel attention convolution module, wherein the multi-scale channel attention convolution module focuses on capturing low-level spatial-temporal characteristics with discrimination from original motor imagery electroencephalogram signals (MI-EEG); s3, segmenting the electroencephalogram signal into a plurality of local time sequence segments by sliding the window; s4, inputting the data output by the sliding window into the time fusion residual network in parallel so as to further extract advanced time features from the time sequence; and S5, fusing the high-order time sequence characteristics of all windows through a full connection layer, and outputting probability prediction of a motor imagery task through a Softmax function. The method can effectively solve the problems that convolution scale division of the motor imagery network is limited, and features of all channels and advanced time features are ignored, and the average classification accuracy of the motor imagery electroencephalogram signals is improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm control method and system based on motor imagery and eye movement tracking. (2) constructing an electroencephalogram signal classification model and performing training; (3) calibrating an eye movement tracking module and a mechanical arm control module; (4) inputting data collected in real time into an electroencephalogram signal classification model and an eye movement tracking module, and generating a user intention and a to-be-grabbed target label; and (5) an instruction decision is made according to the intention of the user, the to-be-grabbed target label is input into a mechanical arm control module, a mechanical arm control instruction is generated, the mechanical arm control instruction is driven to execute the grabbing action, and the control process of the mechanical arm is achieved. According to the scheme, more efficient man-machine interaction is achieved, through combination of motor imagery electroencephalogram decoding and eye movement tracking, mechanical arm selection target grabbing operation can be conducted through brain control and eye movement without manual operation, and the real-time performance and stability of a control system are improved through optimization of the decoding process.

The invention discloses a motor imagery electroencephalogram signal decoding method and system based on a Sinc filter, and belongs to the field of brain-computer interfaces, and the method comprises the following steps: carrying out convolution calculation on a motor imagery electroencephalogram signal and the Sinc filter to obtain a frequency characteristic signal of the motor imagery electroencephalogram signal; performing channel connection on the characteristic signals of the motor imagery electroencephalogram signals of different frequency bands to generate high-dimensional electroencephalogram characteristic signals; obtaining a parallel multi-branch space-time convolutional network, performing time domain feature extraction, spatial feature extraction and average pooling dimension reduction processing on the high-dimensional electroencephalogram feature signal to obtain a channel weight, and performing weighted calculation on the target feature map based on the channel weight to obtain a space-time feature map; and obtaining class labels of the motor imagery electroencephalogram signals after mapping processing and activation processing, and obtaining a decoding result. According to the method, the Sinc filter and the space-time convolutional neural network are combined, and the accuracy and robustness of motor imagery electroencephalogram signal classification are improved.

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.

The invention relates to the technical field of brain-computer interfaces and neural signals, in particular to a light-weight electroencephalogram signal decoding method based on spatial grouping enhancement, which comprises the following steps: acquiring motor imagery electroencephalogram signal data, and preprocessing the motor imagery electroencephalogram signal data; constructing a space grouping enhancement network model, inputting the preprocessed motor imagery electroencephalogram signal data for training, calculating an importance coefficient, determining a loss function, and marking a corresponding motor imagery category; and obtaining to-be-decoded motor imagery electroencephalogram signal data, inputting the to-be-decoded motor imagery electroencephalogram signal data into the trained space grouping enhancement network model, and performing decoding in combination with the importance coefficient to obtain a corresponding classification result. According to the space grouping enhancement network model, the space-time characteristics of the EEG signals can be synchronously optimized, coupling optimization of the space-time characteristics of the EEG signals is achieved, the technical problem that an existing EEG signal decoding method is difficult to balance between model complexity and classification precision is solved, and the decoding accuracy and real-time performance are improved.

The invention discloses a motor imagery early fusion decoding method based on EEG-fNIRS. The method comprises the steps that EEG and fNIRS signals in a motor imagery task are acquired and preprocessed; eEG and fNIRS feature extraction and alignment modules are used for extracting and aligning EEG feature information and fNIRS feature information respectively; performing deep fusion on the EEG and fNIRS time dimension alignment features by using a bidirectional cross attention module to obtain EEG-fNIRS early fusion features; the EEG-fNIRS early fusion features are input into a Transform encoder, different time step information is fused in a self-adaptive mode through an attention weighted pooling module, and EEG-fNIRS fusion features are obtained; and inputting the EEG-fNIRS fusion feature into a multi-layer perceptron to output a motor imagery task category. According to the method, space-time coupling characteristics of EEG and fNIRS signals can be fully utilized, deep fusion of cross-modal characteristics is realized, and the decoding performance of a motor imagery task is remarkably improved.

The invention relates to the field of biological signal processing, and particularly discloses a classification method for intrusive electroencephalogram signal motor imagery tasks, which comprises the following steps of: acquiring original electroencephalogram signals according to task requirements through an intrusive brain-computer interface system; preprocessing the electroencephalogram signal data, and removing noise in combination with a signal-to-noise ratio; performing independent component analysis to extract an electroencephalogram signal source; signal features are extracted manually, and principal component analysis dimension reduction is carried out; and putting the data into the model for training, and classifying the signals. According to the method, noise and redundant information of the electroencephalogram signals are effectively removed through combination of ICA and PCA, and the influence of artifacts on a classification result is reduced, so that the representative characteristics of the signals are improved, the classification accuracy is improved, and a good classification effect can still be ensured especially in a high-noise environment.

The invention provides an electroencephalogram text cross-modal brain intention decoding method based on generative text prompt. International public motor imagery data sets BCI IV 2a and BCI IV 2b are adopted for validity verification, and the method comprises the steps that firstly, a public data set is preprocessed, and a training set and a test set are established; secondly, constructing an electroencephalogram text cross-modal brain intention decoding model based on generative text prompt; inputting the preprocessed training set into a model, and carrying out model training; and finally, inputting the test set into the trained model, and carrying out model performance test. The method has the advantages that the motor imagery task text description is generated by utilizing the generative language model, and the model is guided to understand the correlation between the motor imagery task context and the electroencephalogram text data; an electroencephalogram text multi-scale cross-modal attention feature fusion strategy is designed, and effective complementation of multi-modal brain intention information is achieved. Effectiveness verification is carried out on the method on BCI IV 2a and BCI IV 2b, the average recognition accuracy rate reaches 85.32% and 87.69% respectively, and both the average recognition accuracy rate and the average recognition accuracy rate are superior to those of an existing optimal method.

The invention discloses an end-to-end deep learning method and device based on EEG motor imagery classification and a medium. The method comprises the following steps: acquiring an electroencephalogram signal, and inputting the electroencephalogram signal into an MBCNet model; the MBCNet model comprises a first branch and a second branch, and the first branch and the second branch have the same structure and different convolution kernel sizes; respectively extracting space-time information of the electroencephalogram signal through the first branch and the second branch to obtain a first space-time feature and a second space-time feature; performing feature fusion on the first spatial-temporal feature and the second spatial-temporal feature to obtain fused feature representation; and performing feature classification on the fused feature representation to obtain a motor imagery result represented by the electroencephalogram signal. The end-to-end deep learning method and device based on EEG motor imagery classification and the medium disclosed by the invention have good performance in electroencephalogram signal motor imagery classification decoding; the provided MBCNet model has good robustness and generalization ability, and compared with other baseline models, the provided MBCNet model has better decoding ability.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to an exercise rehabilitation system and method based on layered stimulation navigation and a closed-loop brain-computer interface, and the exercise rehabilitation system comprises a layered stimulation navigation module, a brain-computer interaction module, a multi-channel functional electrical stimulation module and an exercise feedback module. The layered stimulation navigation module outputs different stimulation schemes for the superficial layer muscle stimulation area and the deep layer muscle stimulation area; the brain-computer interaction module is used for outputting a decoding result capable of representing the motion intention of the convalescent; the multi-channel functional electrical stimulation module calls a stimulation scheme according to a decoding result, loads an injection current to a working electrode determined by the stimulation scheme, implements electrical stimulation and generates a motion response; the motion feedback module collects and fuses the multi-mode feedback signals under the motion response, generates a stimulation parameter adjustment signal used for adjusting a stimulation scheme executed in the previous rehabilitation period, and outputs a neural feedback signal used for guiding the rehabilitation person to adjust the motion imagination.

The invention belongs to the related technical field of accurate decoding of motor imagery brain-computer interface signals, and particularly relates to a decoding model construction method and decoding method of motor imagery brain-computer interface signals, comprising the steps of constructing a knowledge data fusion network KDFNet which comprises a time domain convolution module, a space domain convolution module, a feature engineering module and a classification module; the time domain convolution module comprises m time domain convolution kernels, different time domain convolution kernels correspond to different frequency bands, and each time domain convolution kernel is used for performing time domain convolution on each channel signal; the spatial domain convolution module comprises m * n spatial domain convolution kernels, and the output of one time domain convolution kernel is correspondingly subjected to further spatial domain convolution processing by n spatial domain convolution kernels; the feature engineering module is used for obtaining spectrum power knowledge features based on the output of each airspace convolution; the FBCSP model is trained in a supervised mode to obtain parameter values of a classifier in the model, and the parameter values are used for initializing parameters of a full connection layer in a classification module; and the KDFNet is trained in a supervised manner. According to the invention, decoding can be realized efficiently.

The invention provides a few-sample motor imagery electroencephalogram signal classification method. In a source domain pre-training stage, a source domain data set, a feature mapping module and a classification module are constructed, the feature mapping module and an auxiliary classifier are pre-trained, and the pre-trained auxiliary classifier is discarded. In the meta learning stage, a source domain data set is divided into a meta training task set, based on a cross-data-set motor imagery decoding framework, task parameters are optimized on a support set through internal circulation gradient descent, loss is calculated in a query set to update meta parameters, a feature mapping module and a classification module are jointly trained, and the adaptability and generalization ability of the model are enhanced. In the target domain verification and classification stage, a small number of electroencephalogram signals of a target domain user are collected to serve as an adaptation set, and the motor imagery electroencephalogram signal classification efficiency and accuracy are improved through an internal circulation gradient descent fine tuning model based on parameter initialization in the meta-learning stage; source domain pre-training lays a foundation, meta-learning enhances adaptability, and target domain fine tuning realizes accurate classification.

The invention belongs to the related technical field of neural rehabilitation engineering, and particularly relates to a cerebral apoplexy rehabilitation method based on fusion of a motor imagery brain-computer interface and transcranial direct current stimulation, which comprises the following steps: acquiring a corresponding artifact template based on transcranial direct current stimulation parameters; carrying out source separation on the collected electroencephalogram test times by adopting a joint blind source separation algorithm to obtain clean electroencephalogram signal components; wherein the construction mode of the artifact template library comprises the following steps: extracting electroencephalogram trial artifact samples of multiple stimulation periods, and generating a transcranial direct current stimulation artifact template through an average analysis or principal component analysis mode to obtain the artifact template library; performing motion intention decoding on the clean electroencephalogram signal component to obtain a motion intention of the patient; inputting the motion intention, pre-collected patient body state evaluation information and patient feedback comfort into a fuzzy system algorithm to obtain stimulation parameters of to-be-applied transcranial direct current stimulation, and realizing active rehabilitation through the transcranial direct current stimulation. According to the invention, algorithm-driven closed-loop rehabilitation regulation and control can be realized.

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.

The invention provides a motor imagery-based rehabilitation evaluation method and system and a storage medium, and the method comprises the steps: obtaining electroencephalogram signal data collected by a subject during the execution of a motor imagery-based rehabilitation task, and extracting the frequency domain features of the electroencephalogram signal data; inputting the frequency domain characteristics of the electroencephalogram signal data into a pre-trained encoder for encoding, and outputting to obtain embedded representation of the electroencephalogram signal data; inputting the embedded representation of the electroencephalogram signal data into a natural language decoder, and outputting to obtain a rehabilitation evaluation result of the subject; wherein in the process of training the signal encoding module to obtain the encoder, the signal encoding module is updated through encoding loss, so that the embedding representation of the label text and the embedding representation of the corresponding electroencephalogram signal data realize cross-modal alignment. According to the method, the end-to-end generation from the original electroneurographic signal to the multi-modal evaluation information can be realized.

The invention provides a rehabilitation training plan processing method, a brain-computer interface device and a storage medium, and relates to the technical field of brain-computer interfaces. According to the method, the motor imagery ability evaluation result of the target object is obtained mainly based on the electroencephalogram signal of the target object, that is, the motor imagery ability of different patients can be quantitatively evaluated according to the scheme; then, aiming at the two indexes of the motor imagery ability evaluation result and the dyskinesia level of the patient, a rehabilitation training plan is generated for the patient in a personalized manner, namely, the rehabilitation plan has the characteristics of pertinence, scientificity, reasonability and the like, so that the rehabilitation efficiency of the patient is greatly improved, and the rehabilitation time of the patient is saved; meanwhile, the use efficiency of the rehabilitation robot is improved, that is, the rehabilitation effect of the motor imagery therapy is better than that of a traditional rehabilitation method, and the problem that in the prior art, rehabilitation training is unreasonable in formulation is solved.

The invention discloses a brain-controlled rehabilitation equipment-oriented control strategy generation method, device and equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the following steps: acquiring a multi-channel electroencephalogram signal, and preprocessing the multi-channel electroencephalogram signal to obtain a target electroencephalogram signal comprising a steady-state visual evoked potential signal and a motor imagery signal, the target electroencephalogram signal corresponds to a preset action category; decoding the steady-state visual evoked potential signal by adopting filter group task related component analysis to obtain a correlation score vector; decoding the motor imagery signal by adopting a Mangban dynamic routing space-time network model to obtain a classification score vector; performing posterior probability distribution conversion and weighted fusion on the correlation score vector and the classification score vector to obtain fusion probability distribution; and generating a target control strategy according to the action category corresponding to the highest probability value in the fusion probability distribution. The control strategy obtained by the invention can consider both intention recognition precision and rehabilitation nerve activation effect.

The invention relates to the field of deep learning, in particular to a unilateral motor imagery electroencephalogram signal recognition method and system based on attention time convolutional network multi-feature fusion. Performing shallow feature extraction on the processed brain wave signal to obtain a shallow spatial-temporal feature and a shallow time-frequency feature; performing deep feature extraction on the shallow spatial-temporal features and the shallow time-frequency features based on a multi-level processing architecture to obtain joint characterization; fusing the joint characterization to obtain a stable classification decision; and performing classification processing on the stable classification decision to obtain a classification result of the unilateral motor imagery electroencephalogram signals. According to the method, the attention mechanism and the time convolutional network are integrated, and the space-time and time-frequency feature fusion architecture is combined, so that the problems of weak difference, non-stationarity and insufficient multi-domain feature utilization of unilateral motor imagery electroencephalogram signals are effectively solved.

The invention provides a motor imagery electroencephalogram signal classification method, system and device based on multi-Riemannian kernel fusion features and a medium, and relates to the technical field of electroencephalogram signal classification, comprising: constructing and training a multi-Riemannian kernel fusion feature classification model for a subject; to-be-classified MI-EEG signals of a subject are collected and preprocessed, and optimized MI-EEG signals are obtained; performing multi-Riemannian kernel feature extraction on each section of optimized MI-EEG signal to obtain three kernel feature matrixes, performing centralization, dimension reduction and normalization processing in sequence, and performing feature fusion to obtain a fused feature matrix; and inputting the fusion kernel feature matrix into a multi-Riemann kernel fusion feature classification model to carry out category prediction so as to obtain a category label of the MI-EEG signal. According to the method, the kernel features of the MI-EEG signals on the multiple manifolds are fused, meanwhile, the influence of body difference is fully considered, richer and more comprehensive feature information is provided for classification, and the interpretability, accuracy and reliability of classification are improved.

The invention discloses a brain-computer interface algorithm system based on lower limb motor imagery, relates to the technical field of brain-computer interfaces, and is technically characterized in that a high-precision lower limb motor imagery brain-computer interface system based on self-adaptive time frequency-Riemannian geometry fusion is adopted, and through optimal frequency band selection and covariance manifold classification, a high-precision lower limb motor imagery brain-computer interface algorithm is obtained. Robust decoding of the motion intention of the patient suffering from knee joint pain is achieved, and reliable technical support is provided for clinical rehabilitation.

The invention discloses a cross-subject movement phenomenon electroencephalogram signal classification method based on TSLANet and Riemannian geometric features, and relates to the technical field of electroencephalogram signals. The classification method is a new motor imagery electroencephalogram classification method based on a deep learning model, the method comprises the steps that firstly, original electroencephalogram signals are subjected to filtering and normalization processing to serve as input of the novel deep learning model RMETNet, the RMETNet combines a convolutional network and a TSLANet network, complex time sequence features are extracted through the TSLANet, and then the complex time sequence features are extracted through the deep learning model RMETNet; meanwhile, Riemannian geometric features are learned through a multi-scale convolution module, and the RMETNet combines time sequence features with features after Riemannian geometric alignment, so that feature expression is enriched, the accuracy of motor imagery electroencephalogram classification is improved, in order to reduce cross-subject distribution feature differences, MMD loss is introduced in the training process of the RMETNet, and the accuracy of motor imagery electroencephalogram classification is improved. The distribution consistency between the source domain and the target domain is enhanced, so that the effect of the cross-subject experiment is improved, the superiority of the proposed model is evaluated by carrying out the single-subject experiment and the cross-subject experiment on the two common data sets, and a good result is obtained.

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.

The invention provides a brain-computer interface-based TMS and VR collaborative rehabilitation training system, which is characterized in that a multi-mode collaborative positioning module is used for collecting VR interaction data and electroencephalogram data and determining the optimal stimulation state of a subject; the motion intention analysis module is used for matching a rehabilitation mode according to the collected data and in combination with the type of the subject; and the closed-loop rehabilitation interaction module is used for adjusting parameters of the TMS equipment according to the rehabilitation mode and stimulating the final stimulation area under the optimal stimulation state of the subject. For a mild stroke patient, the system dynamically monitors the plastic change of the autonomic nerve and adaptively adjusts the treatment parameters according to the plastic change of the autonomic nerve; for a severe stroke patient, the virtual reality situation is combined to effectively induce motor imagery and synchronously collect and analyze electroencephalogram signals, closed-loop nerve regulation is achieved, synchronous accurate intervention and curative effect optimization of the severe stroke patient are achieved, and an intelligent solution is provided for rehabilitation treatment of the nerve function of the severe stroke patient.

The invention discloses a motor imagery electroencephalogram signal classification method based on a multi-scale convolutional neural network, and belongs to the technical field of brain-computer interfaces, and the method comprises the following steps: carrying out band-pass filtering, channel selection and standardized preprocessing on an original EEG signal; a multi-scale convolutional neural network model is constructed, multi-resolution time features are extracted through a shallow-layer multi-scale convolutional block, spatial-temporal feature alignment and cross-scale splicing are realized through a middle-layer convolutional layer, and finally a classification probability is adaptively generated through a dynamic full-connection layer; an independent subject training strategy is adopted to optimize model parameters, and classification and recognition of motor imagery tasks are achieved through adaptive feature dimension processing. According to the method, the time-frequency features of EEG signals are effectively captured through multi-scale convolution kernel design, key brain region response is enhanced in combination with a feature fusion strategy of space attention guidance, individual difference influences are eliminated through dynamic parameter initialization, and the feature expression ability and cross-subject generalization performance of a classification model are remarkably improved.