Research on EEG recognition method based on common spatial patterns and transfer learning / 国际生物医学工程杂志

ABSTRACT

Objective:Aiming at the problem of target user electroencephalogram (EEG) recognition, an EEG recognition method was presented based on common spatial patterns (CSP) and transfer learning.Methods:Firstly, preprocess was adopted on the original EEG data, and time windows 0.5~2.5 s and broad frequency band 8~30 Hz EEG signals, which contained α and β wave, were selected. Here event-related desynchronization (ERD) phenomenon existed significant differences. Afterwards, by CSP preprocessed EEG signals of multi-user were conducted to extract feature and feature vectors were obtained, respectively. Finally, by transfer learning target user EEG recognition was completed.Results:In channel Cz, ERD of right hand motor imagery was higher than ERD of foot motor imagery. The classification accuracy of users aa, al, av, aw, and ay were 93.8%, 100.0%, 84.2%, 94.6%, and 94.4%, respectively. The average classification accuracy was 92.4%, which was better than the commonly used classifiers SVM and EM. The method was only lower than the method of the first winner in the competition adopted by Tsinghua University 1.8%.Conclusions:EEG recognition method based on CSP and transfer learning increased target user EEG recognition performance by using non-target users and had important implications for the study of motor imagery brain-computer interface.