164 results about "Motor imagery" patented technology

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 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 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 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 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 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 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.

The invention discloses an electroencephalogram signal decoding method and system based on characteristic decomposition and adversarial training, and relates to the technical field of brain-computer interfaces and neural engineering. Then, the extracted features are decomposed into target feature vectors and irrelevant feature vectors through a feature decomposition module; information related to a motor imagery task is reserved in a target feature vector through supervised learning of a classification module, meanwhile, task related information is not included in an irrelevant feature vector through confrontation training of a judgment module and a feature decomposition module, and therefore separation of target features and individual irrelevant features is achieved. In the test stage, only the target feature vectors are used for motor imagery task classification, individual differences are effectively overcome, and the generalization ability of the model across subjects is improved.

The invention discloses a motor imagery electroencephalogram decoding method based on multi-view space-time convolution and an attention mechanism, which comprises the following steps of: firstly, constructing multi-view data by utilizing a filter bank, and enhancing training data by adopting a frequency band sensing fragment splicing strategy so as to expand sample distribution; secondly, respectively and accurately capturing specific time sequence features and spatial features of different frequency bands through a frequency adaptive convolution kernel and depth separable convolution; meanwhile, in combination with a channel attention mechanism and a time sequence statistics module, feature channel weights are effectively utilized, and a mean value and a logarithmic variance in a time window are calculated, so that the modeling capability of the model for key spatio-temporal information is enhanced. According to the method, the problem of insufficient spatio-temporal feature mining in the motor imagery task is effectively solved, and a competitive result is obtained.

The invention discloses a brain-computer interface manipulator motion detection system for disturbance of consciousness assessment, which belongs to the technical field of disturbance of consciousness assessment and specifically comprises an electroencephalogram acquisition device for acquiring original electroencephalogram signals when a patient executes motor imagery tasks containing different hand action instructions; the dynamic feature extraction module generates a motor imagery activation map by calculating the energy distribution change of a preset frequency band; the driving instruction generation module calculates the space-time matching degree of the map and a preset action instruction and generates a manipulator driving instruction; the manipulator control module is used for controlling and executing a composite action positively correlated with the instruction strength, and the action is formed by combining basic grasping and multi-joint coordination actions in proportion; the motion capture device records track changes, and the ratio of the number of the joints exceeding the threshold value to the motion duration serves as a consciousness activity index; the disturbance of consciousness grading module determines a grading result according to the fluctuation range of the index in the continuous period; the method provides an objective and accurate quantification means for disturbance of consciousness assessment.

The invention belongs to the technical field of brain-computer interface and neural signal processing, and particularly relates to a motor imagery electroencephalogram signal recognition method based on multi-scale space-time convolution, which comprises the following steps of: 1, performing multi-band filtering and data enhancement processing on an original electroencephalogram signal to generate enhanced electroencephalogram data; 2, constructing a multi-scale time domain convolutional network, and extracting features of different time scales; step 3, carrying out weighted fusion on the spatio-temporal features by using an attention mechanism, and enhancing expression of key features; and 4, constructing a classification module, carrying out global time sequence compression on the coding features, and mapping the coding features to a motor imagery category probability through a full connection layer. According to the method, the spatial-temporal characteristics of the EEG signals can be comprehensively extracted, so that the recognition accuracy can be improved, key information can be highlighted by using an attention mechanism, so that the discrimination capability of the model can be enhanced, and the method can also improve the cross-subject robustness and generalization performance, so that the method is more suitable for practical application.

The invention discloses a rehabilitation system and method based on motor imagery electroencephalogram signals and eyeball tracking. The rehabilitation system comprises a signal acquisition module used for acquiring the motor imagery electroencephalogram signals and recording limb state information; the signal processing module is used for storing the collected motor imagery electroencephalogram signals, determining a motor intention through the motor imagery electroencephalogram signals and outputting a selection instruction; the eyeball tracking module is used for tracking an eyeball watching interface prompt instruction and cooperatively sending a treatment instruction with the selection instruction; and the electrical stimulator module is used for receiving the treatment instruction and outputting stimulation pulse current according to the treatment instruction. Through cooperation of real-time collection of non-invasive motor imagery signals and an eyeball tracking technology, the defects that a single motor imagery electroencephalogram signal is few in classification mode and low in real-time classification precision are overcome, rehabilitation training modes of different parts are autonomously selected according to the intention of a subject, the initiative and enthusiasm of a patient in the rehabilitation training process are improved, and the rehabilitation training efficiency is improved. The rehabilitation treatment effect of the patient is effectively improved.

This application discloses a method, apparatus, device, and storage medium for classifying users of a motor imagery brain-computer interface. The method includes using independent component analysis (ICA) to decompose the user's target EEG signal, obtaining multiple initial independent components. Feature data is extracted from each initial independent component. Based on the feature data, a first target independent component corresponding to contralateral event-related desynchronization and a second target independent component corresponding to ipsilateral event-related synchronization are determined from the initial independent components. Finally, the classification result of the motor imagery brain-computer interface user is obtained. This scheme improves the accuracy and reliability of classification by using ICA-related neurodynamic modeling and classifying users based on multi-dimensional neural indicators obtained from feature data. Furthermore, by combining multiple feature data for automated selection of independent components, the subjectivity issues caused by manual intervention are avoided, improving the applicability and scalability of the classification method.

A motor imagery electroencephalogram signal classification method based on multi-head self-attention mechanism and parallel convolution belongs to the field of computer software. In view of the problem that the low signal-to-noise ratio of the electroencephalogram signal leads to difficult feature extraction, an improved network model based on EEGNet is proposed, which is referred to as EEG-MATCNet. First, the original electroencephalogram signal is subjected to preliminary feature extraction by using a parallel convolution layer, and different scale convolution kernels can extract time features of different time steps. At the same time, the attention weights of the electroencephalogram signals between the electrodes are calculated by using a multi-head self-attention mechanism, so that the network can better extract spatial features during training. In addition, the receptive field of the convolution kernel is improved by using a time convolution network, so that the model can extract higher-level time features. Experiments prove that the classification method proposed in the application can more effectively improve the feature extraction and classification performance of the motor imagery electroencephalogram signal.

The invention discloses a motor imagery electroencephalogram signal classification method and device, a terminal and a storage medium, and relates to the field of biomedical signal processing.The method comprises the steps that electroencephalogram signals based on user motor imagery are obtained and preprocessed, and electroencephalogram features are determined; performing multi-scale spatio-temporal feature extraction and space and channel decoupling reconstruction on the electroencephalogram features, and determining target spatio-temporal enhancement features; and classifying the target space-time enhancement features through a classification output layer, and determining a classification result. Due to the fact that space and channel decoupling reconstruction is carried out on the features, redundant correlation of the cross-electrode electroencephalogram signals is systematically eliminated, and the problems that in the prior art, space and channel information are jointly processed, information redundancy is caused, and calculation burden is increased can be effectively solved.

The invention provides a brain-computer interface motor imagery real-time control method and system fusing multi-modal feature extraction and dynamic decision, electronic equipment and a storage medium, and the method comprises the steps: obtaining a high-temporal-spatial-resolution electroencephalogram signal through a multi-channel electroencephalogram signal collection device, and covering a target brain region to support a motor imagery task; the system preprocesses the electroencephalogram signals and generates high-quality input data through filtering and standardization; a multi-modal feature extraction method is adopted, and spatial domain and time frequency features are fused to generate a comprehensive feature vector; generating classification probability distribution according to the comprehensive feature vector through a dynamic decision-making mechanism; based on confidence threshold judgment, outputting a control instruction or triggering a re-calibration process; and through real-time feedback and personalized adjustment, the motor imagery control performance is optimized. According to the method, the control precision and the response speed of the brain-computer interface system are remarkably improved by fusing the multi-dimensional features and a dynamic decision-making mechanism.

The application discloses a complex action-based spatio-temporal frequency domain feature fusion action recognition method and system. The application preprocesses the electroencephalogram signal of the complex action motor imagery paradigm, extracts the global spatio-temporal feature by using a time-space partition branch network TG-Net, extracts the frequency domain feature by using a frequency branch network F-Net for the power spectrum density of the preprocessed signal, and performs action recognition after flattening, splicing and the like of the frequency domain feature and the global spatio-temporal domain feature. The application constructs a time-space frequency domain double branch network, deeply fuses and splices the global spatio-temporal feature and the power feature, breaks through the limitation of the traditional method which is limited in the time-space domain, realizes the complementary feature extraction of the motor imagery electroencephalogram signal in the time, space and frequency dimensions, and adopts the partition weighting splicing mode of the functional brain area in the space dimension to highlight the important channel action recognition method, so that the comprehensive feature is acquired, and the classification precision is improved.

The application discloses a motor imagery electroencephalogram signal denoising method and device, medium and product, relates to the technical field of deep learning and biomedical signal processing, and the method comprises the following steps: acquiring a target electroencephalogram signal containing artifacts; inputting the target electroencephalogram signal containing artifacts into a trained electroencephalogram denoising model to obtain a final denoised electroencephalogram signal; wherein the electroencephalogram denoising model comprises an electroencephalogram denoising branch, an artifact prediction branch and an artifact representation interaction attention fusion reconstruction module; the electroencephalogram denoising branch comprises a multi-scale self-adaptive enhancement module, a frequency domain dynamic enhancement module and a feature extraction module. The application solves the problems of traditional electroencephalogram denoising methods in signal fidelity, spectral fidelity, spatial structure preservation and generalization ability.

The application discloses a kind of fNIRS motor imagination decoding methods based on double-flow cross attention and function connection fusion, obtain the fNIRS original signal of subject under motor imagination task, calculate HbO signal and HbR signal concentration variation sequence, respectively to HbO signal and HbR signal are first-order differential processing, generate the enhanced feature stream reflecting the change rate of HbO signal and HbR signal, and introduce residual connection;Double-flow feature encoder is constructed, and HbO signal depth feature map and HbR signal depth feature map are extracted using time convolution and depth space convolution respectively;Cross attention module is introduced, and the depth feature after double-flow dynamic complementary fusion is obtained;For each signal sample obtained, calculate the pearson correlation coefficient matrix between all acquisition channels, and flatten it into a global brain network connection vector matrix;The depth feature after double-flow dynamic complementary fusion is spliced with the global brain network connection vector matrix, and the category result of motor imagination is output through fully connected classifier.

The application discloses a kind of based on deep manifold's electroencephalogram motor intention decoding method, system and device, it is related to brain-computer interface technical field.The method includes the following steps: obtaining original motor imagery electroencephalogram signal, and motor imagery electroencephalogram signal is preprocessed and feature extraction;Utilize spatial attention mechanism under the premise of keeping manifold geometric structure to the electroencephalogram channel correlation is weighted, then from time and spatial dimension respectively to the weighted covariance matrix sequence is mixed with information, simultaneously utilize symmetric positive definite residual link to retain underlying geometric information, obtain high-level geometric feature;Utilize tangent space mapping to convert high-level geometric feature to Euclidean space, and obtain the recognition result of motor imagery by classifier.The application can realize the adaptive weighting of electroencephalogram channel importance, can also retain electroencephalogram geometric feature while aggregating electroencephalogram time information, improves the robustness and accuracy of electroencephalogram decoding.

The motion imagination device control method, device and system, the method comprises the following steps: inputting the brain electrical signal of a wearer collected by a motion imagination device into a basic feature extraction model to obtain corresponding feature information; inputting the feature information into at least one classification and recognition model corresponding to a motion action to obtain the freedom degree discrimination result of each action; and generating the activity instruction of the motion imagination device based on all the freedom degree discrimination results. The present application separates the model, fully utilizes the existing data to realize the basic motion imagination recognition function, does not need to use a random initial model and specially train the user, divides the neural network model on the device into two parts of basic feature extraction and classification and discrimination, and can make the model more robust.

The application belongs to the technical field of electric digital data processing, and particularly relates to an adaptive ensemble learning model optimization method for electroencephalogram signal classification, which comprises the following steps: acquiring a multi-channel electroencephalogram sample segment, extracting features containing time-frequency energy, phase locking and spatial mode, the phase locking containing phase locking values among the multi-channels, and constructing three base learners; constructing a weighted brain network through the phase locking values and constructing a brain state feature vector; taking the fusion weight of each base learner as an optimization variable, combining the weight into a particle, grouping according to feature preferences, constructing fitness, iteratively updating the particle based on the fitness, and iteratively optimizing to obtain an optimal weight; weighting and fusing the prediction probabilities of each base learner by using the optimal weight, selecting the category corresponding to the maximum prediction probability as the classification result, and triggering re-optimization based on the change of the adjacent brain state feature vector. The method realizes online adaptive optimization of the weight, and improves the accuracy and long-time stability of motor imagery electroencephalogram signal classification.