Domain adaptive method for solving subject difference in motor imagery brain-computer interface

By adopting the domain adaptive manifold embedded distribution alignment feature mapping method in the motor imagination brain-computer interface, the problem of low classification accuracy caused by subject differences is solved, and the recognition accuracy and robustness are significantly improved, providing an effective solution for the further development of MI-BCI technology.

CN119987544AActive Publication Date: 2025-05-13HARBIN INST OF TECH
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Patent Information

Application Number
CN202510063312.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-15
Publication Date
2025-05-13
Estimated Expiration
2045-01-15

AI Technical Summary

Technical Problem

Due to the difference in electroencephalogram signals between subjects in the motor imagination brain-computer interface, the classification accuracy of the MI-BCI system is low, which has become the main obstacle to the further development of technology.

Method used

A domain adaptive method is proposed. Through the online brain-computer interface system, multiple motion imagination tasks are performed, the subject's EEG signal data is collected, preprocessed and feature extraction is performed, and the domain adaptive manifold embedding distribution alignment feature mapping method is used to project the data of the training set and the target domain, construct a classifier and perform feature fusion to improve classification accuracy.

Benefits of technology

It significantly improves the recognition accuracy and robustness of sports imagination tasks, effectively avoids the problem of low classification accuracy of MI-BCI system caused by differences in subjects, and lays a solid foundation for the application of related fields.

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Abstract

The invention discloses a domain adaptive method for solving subject differences in a motor imagery brain-computer interface, and belongs to the technical field of transfer learning in the motor imagery brain-computer interface. According to the method, the problem of low classification accuracy of the MI-BCI system caused by electroencephalogram signal difference between subjects is solved. According to the method, the information of source domain and target domain samples is fully utilized, and the recognition precision and robustness of the motor imagery task are remarkably improved in combination with data processing and a classification algorithm. Through efficient data preprocessing and a feature extraction method in domain adaptive manifold embedding, the consistency of data feature mapping of a training set and a target domain is ensured. The classifier optimization based on the structural risk minimization principle further enhances the classification performance. Through a feature fusion and voting mechanism, the credibility of label classification is effectively improved, and the method can effectively avoid the problem of low classification accuracy of an MI-BCI system caused by electroencephalogram signal difference between subjects. The method can be applied to electroencephalogram signal classification in a motor imagery brain-computer interface.
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