254 results about "Motor imagery" patented technology

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 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 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 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 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 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 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 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 decoding method, system and device based on a double-path hierarchical hybrid architecture. The method comprises the following steps: acquiring a multi-channel motor imagery electroencephalogram signal; extracting the preliminary spatio-temporal features through a convolution embedding module to obtain embedded features; the embedded features are input into a double-path hierarchical mixing module formed by stacking a plurality of feature processing sub-modules, feature extraction is performed on the embedded features layer by layer through a main path and an auxiliary path which are arranged in parallel, and different feature extraction strategies are adopted according to stacking hierarchies; in each layer, the dual-path output features are fused through an adaptive fusion mechanism to obtain depth features; and finally, outputting the prediction probability of the motor imagery category through a classifier module. The method effectively solves the problems that an existing method is high in calculation complexity, unbalanced in feature extraction and insufficient in robustness, and the decoding precision and efficiency of the motor imagery electroencephalogram signals are remarkably improved.

The invention provides a user intention classification method based on a multi-task learning time-frequency double-branch network, and the method comprises the steps: data preprocessing: employing LaBrM to extract motor imagery and N-back task features from an original EEG signal, and generating a final EEG feature embedding vector; based on an adaptive spectrum feature fusion attention module and a multi-scale expansion factor convolution time feature extraction module, features of EEG feature embedding vectors are extracted respectively; fusing the extracted time features and spectrum features; and classifying the fused features through a multi-task classifier. According to the method, the distribution characteristics of the electroencephalogram signals in the time domain and the frequency domain are fully utilized, and accurate recognition of intention information in multiple cognitive tasks of the user is achieved.

The invention requests to protect a motor imagery electroencephalogram signal enhancement method based on graph attention. The method comprises the following steps: firstly, carrying out multi-stage preprocessing on an original electroencephalogram signal, including band-pass filtering and baseline drifting, and removing artifacts in combination with independent component analysis, then, extracting time information of each channel by utilizing a one-dimensional convolutional network so as to capture time sequence characteristics in a motor imagery process, on the basis, constructing a graph attention network, taking the electroencephalogram signal channels as nodes, and taking the electroencephalogram signal channels as the nodes; the method comprises the following steps of: dynamically modeling and optimizing the internal relation between channels, strengthening motor imagery related channels such as C3, C4 and Cz by combining priori knowledge, further introducing a time attention network to identify and enhance key time slices rich in discrimination information, and finally, forming a high-dimensional feature matrix by fusing space-enhanced and time-enhanced electroencephalogram features, so as to realize the recognition and enhancement of the motor imagery related channels. And inputting into a classifier for pattern recognition.

The invention relates to the crossing field of medical rehabilitation and brain-computer interface technology, and particularly discloses a lower limb exoskeleton rehabilitation training system and method based on AR glasses and brain-computer interface control. According to the system, motor imagery electroencephalogram decoding is used as core driving force, lightweight AR visual guidance, lower limb exoskeleton mechanical assistance and functional electrical stimulation feedback are fused, and real-time information interaction among multiple modules is achieved through wireless communication; therefore, a closed-loop rehabilitation system integrating sensing, decoding, execution, multi-mode feedback and self-adaptive adjustment is constructed. Under the framework, the subjective motion intention of the patient not only can directly participate in the exoskeleton control process, but also can continuously adjust training parameters and interaction modes through dynamic monitoring of physiological signals and emotional states, so that a patient-centered lower limb rehabilitation training mode which better conforms to the neuroplastic law is achieved.

The invention relates to the field of electroencephalogram signal processing and neural engineering, and discloses a multi-mode electroencephalogram feature fusion decoding method. The method comprises the following steps: carrying out noise reduction, calibration and standardization processing on an original electroencephalogram signal to generate a standard signal; extracting mu / beta rhythm time-frequency features and Hjorth time-domain features of the signals, and generating a fusion feature vector through PCA dimension reduction fusion; performing pre-classification and weight optimization by using an SVM classifier, performing space and time sequence feature extraction and classification through a CNN-LSTM network, and outputting a classification probability; model parameters are updated according to the probability and verified, and finally real-time control signals suitable for various communication interfaces are generated. According to the method, the accuracy and robustness of electroencephalogram signal decoding are improved, and efficient and self-adaptive motor imagery brain-computer interface control is realized.

The invention discloses a signal fusion processing method for a motor imagery training brain-computer interface, and particularly relates to the technical field of neural signal processing. Multi-channel scalp electroencephalogram data of a user are collected in real time, a time-varying space covariance matrix sequence is constructed through Riemannian geometric space mapping analysis, and a phase amplitude coupling index sequence between different brain rhythms is analyzed and calculated through cross-band coupling dynamics; performing feature hierarchy joint coding on the spatial covariance matrix sequence and the phase amplitude coupling index sequence to generate a real-time neural representation tensor; calculating the distribution divergence of the real-time neural representation tensor relative to the static reference feature manifold in the initial training stage so as to quantitatively represent the mismatch degree; and finally, dynamically adjusting a weighting coefficient and a fusion structure of the multi-modal signal fusion device according to the characterization mismatching degree, and outputting a motion control instruction matched with the current brain state of the user in real time. According to the method, the motion intention decoding precision and stability in the motion imagination training process are improved.

The invention discloses an untrained neural rehabilitation system combining peripheral visual stimulation and motor imagery and a use method thereof. The system comprises a stimulation module, an SSVEP data acquisition module, an SSVEP classification and recognition module, a motor imagery data acquisition module, a motor imagery decoding model training module, a comparison module and a motor imagery recognition accuracy statistical module. The system adopts peripheral visual stimulation as an SSVEP signal induction paradigm, after each visual stimulation, SSVEP signal classification identification is carried out, a classification result is converted into a rehabilitation equipment control command to drive limb training, an active motor imagery task is carried out within 5 seconds later, motor imagery data is collected, the SSVEP signal classification result is used as a training sample label, and the training sample label is used as a training sample label. And after the motor imagery data meets the training sample scale, training a motor imagery decoding model, evaluating the recognition performance of the model on the motor intention, and gradually reducing the dependence on peripheral visual stimulation.

The invention discloses a fusion enhancement online motor imagery intention recognition system and a training method thereof.The method comprises the steps that a first training data set and a second training data set of motor imagery of a user are collected, the first training data set is subjected to a fusion enhancement method containing at least one of noise enhancement, phase enhancement, frequency band enhancement and channel enhancement, and the fusion enhancement method can be implemented independently or in series; generating a first enhancement data set based on the initial fusion enhancement parameters, and training an identification model in a first training stage; in the evaluation stage, identifying a second training data set by using the model, and generating a second enhanced data set for correctly identified data by using the same fusion enhancement method and parameters; in the second training stage, the two types of enhanced data sets are combined to re-train the model for optimization; and meanwhile, traversing the fusion enhancement parameter space, and determining optimal parameters containing various enhancement parameters. Through multiple selectable data enhancement, online loop optimization and automatic parameter optimization, the problems that motor imagery electroencephalogram data is small in sample size, uneven in distribution and high in timeliness are effectively solved.

The invention discloses a self-adaptive optimization method and system for online decoding of a motor imagery brain-computer interface, and the method comprises the steps: carrying out the real-time self-adaption of an electroencephalogram data stream of a target user through a teacher-student model framework on the premise that the privacy protection of source domain training data does not need to be accessed; performing batch weight normalization during testing, decoupling normalization statistic updating and parameter optimization by stopping gradient operation, and stabilizing feature representation; a dynamic category specific entropy threshold mechanism is combined with online category frequency and batch confidence to adaptively screen a high-confidence sample for each category; a dynamic online reweighting strategy is designed, and weights are distributed according to the sample entropy and the category frequency to balance the optimization process; and decoupling contrast learning based on a fixed prototype is introduced, and feature space distribution is optimized. According to the method, the problems of statistic drift, poor fixed threshold adaptability, category imbalance sensitivity, insufficient feature optimization and the like are solved, and the adaptability, the stability and the robustness of cross-user motor imagery brain-computer interface online decoding are improved.

The invention discloses a motor imagery brain-computer interface rehabilitation method fused with upper limb muscle vibration stimulation, which comprises the following steps of: S1, issuing a motor imagery task to a subject, applying bilateral upper limb vibration stimulation, collecting motor-related electroencephalogram signals, extracting time slices of motor imagery and a resting stage, and extracting a motor imagery signal; constructing a motor imagery data set and an idle state data set; s2, performing mark division on the data set, constructing a spatial filter in combination with a vibration stimulation frequency, extracting rhythm features and training a classifier, and obtaining a left-hand, right-hand and idle-state three-classification model; s3, collecting electroencephalogram signals in real time, inputting the electroencephalogram signals into the three-classification model after filtering and feature extraction, outputting classification results and driving pneumatic rehabilitation hands on the corresponding sides, and achieving closed-loop rehabilitation control based on motor imagery and vibration stimulation. The method provides a novel and efficient means for exercise rehabilitation, and is expected to improve the application potential of the brain-computer interface in the field of rehabilitation.

The invention discloses an upper limb rehabilitation training device, method and system and a storage medium, and the method comprises the steps: constructing a virtual scene according to the rehabilitation condition of a patient, and combining rehabilitation paradigms from a paradigm library; high-channel electroencephalogram signals are collected, and real-time quality monitoring is conducted; extracting motor imagery features from the electroencephalogram signals and analyzing the motor imagery features into a motor intention; mapping the motion intention to a virtual limb to realize VR / AR body feedback; and behavior data such as concentration degree are calculated and displayed in real time, and personalized rehabilitation evaluation is completed. By the adoption of the technical scheme, the problems that in existing cerebral apoplexy rehabilitation treatment, the normal form has body deficiency, the training scene is single, medical intervention lacks real-time performance, rehabilitation evaluation is high in subjectivity, and individuation is insufficient are solved.

The invention discloses a motor imagery classification algorithm for perturbing and sensing a double-flow attention mechanism in a Riemannian space, and relates to the technical field of brain-computer interfaces, and the algorithm comprises the steps: mapping an EEG signal to a Riemannian manifold space, modeling an inter-electrode nonlinear relation through the geometric characteristics of the Riemannian manifold space, and combining tangent space dynamic perturbations and a double-flow differential attention mechanism, high-precision classification in a noise environment is achieved, EEG signals are mapped to Riemannian manifold through block covariance matrix calculation, the nonlinear geometrical relationship between electrodes is effectively reconstructed, meanwhile, a double-flow differential attention mechanism based on disturbance flow and reference flow is designed, collaborative optimization of noise suppression and key feature enhancement is achieved, and the noise suppression and key feature enhancement efficiency is improved. And a reliable algorithm support is provided for the practical application of the brain-computer interface system.

The invention relates to a lower limb motor function recovery system and method based on a motor imagery brain-computer interface, and the system comprises a noninvasive electroencephalogram signal collection module which is used for collecting an electroencephalogram signal when a patient carries out a lower limb motor imagery task; the signal processing and intention recognition module is used for carrying out feature extraction and classification recognition on the collected electroencephalogram signals and outputting the motor imagery intention of the patient; the multi-sensory feedback module is used for providing multi-sensory feedback information including visual sense, auditory sense, tactile sense and proprioceptive sense for the patient; the lower limb rehabilitation training module is used for executing lower limb rehabilitation training actions corresponding to the motor imagery intention; the central control module is configured to receive the recognition result of the motor imagery intention and generate a control instruction according to the recognition result to drive the multi-sensory feedback module and the lower limb rehabilitation training module to work cooperatively. According to the system, a multi-sensory collaborative feedback mode is adopted to simulate multi-sensory interaction, and the identification degree and training compliance of a motor imagery task of a patient are improved.

The invention relates to a driver brain-controlled vehicle intention recognition method and system based on neural mechanism guidance. Recording EEG signals of the driver under different vehicle control motor imagery tasks; analyzing the collected EEG data, and identifying a key cortex activation area related to vehicle control; deploying the positions of an fNIRS emission light source and a receiving detector, and obtaining hemodynamic signals; brain region activation analysis is carried out on the collected fNIRS signals, the difference response condition of a key cortex activation region in a motor imagery task is verified, and a motor imagery data set about driver vehicle control is made; constructing a deep learning model, automatically extracting time domain, frequency domain, space and time sequence characteristics of the fNIRS signal, and training the model; the motor imagery brain signals are classified, and the vehicle control intention of the driver is output; according to the method, the physiological interpretability and the model structure rationality of the brain-controlled vehicle system are remarkably improved, and the problem of insufficient generalization caused by blind modeling is avoided.

The application provides a wheelchair control system based on motor imagery electroencephalogram signals and surface electromyogram signals, and relates to the technical field of brain-computer control, comprising a data acquisition module, a system mode selection module, a signal processing module and a decision module; the data acquisition module is used for acquiring physiological signals of a wheelchair user and sending the physiological signals to the system mode selection module; the system mode selection module is used for selecting a processing mode of the physiological signals; the signal processing module is used for pre-processing the physiological signals and extracting signal features for identification; the signal processing module sends an identification result to the decision module; the decision module is used for fusing multiple instruction information and converting the instruction information into a final wheelchair control instruction, and outputting the control instruction to the wheelchair. The application can improve the operation accuracy of the control system, reduce the fatigue of the user when performing motor imagery behavior, improve the user experience and the application performance of the wheelchair control system, and expand the user range of the brain-controlled wheelchair.