Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation.
10.7507/1001-5515.202112023
- Author:
Lizheng PAN
1
;
Yi DING
1
;
Shunchao WANG
1
;
Aiguo SONG
2
Author Information
1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, P.R. China.
2. Provincial Key Laboratory of Remote Measurement and Control Technology, Southeast University, Nanjing 210096, P.R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Channel selection;
Feature representation;
Individual adaptation;
Motor imagery electroencephalogram
- MeSH:
Humans;
Imagination;
Signal Processing, Computer-Assisted;
Brain-Computer Interfaces;
Electroencephalography/methods*;
Imagery, Psychotherapy;
Algorithms
- From:
Journal of Biomedical Engineering
2022;39(6):1173-1180
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
