EEG Classification Based on Multi-domain Features and Random Subspace Ensemble

Authors

Deng Xin, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Can Long, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Jianxun Mi, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Boxian Zhang, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Kaiwei Sun, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Wang Jin, Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China;

Abstract

Abstract: Aiming at the preprocessing feature extraction and classification recognition in BCI system, a method for EEG classification of motion imagery based on random subspaces ensemble learning of multi-domain features is proposed. Based on the analysis on the ERD/ERS characteristics of motion imagery (MI) signals, the multi-domain features of best effective time and frequency bands are extracted as the feature vectors, and the scale of the random subspace ensemble with cross-validation is adaptively chosen, and the EEG classification is realized by using linear discriminant analysis (LDA) classifiers ensemble. The test results show that the accuracy of the multi-domain features and random subspace ensemble can reach 90.71% and the Kappa coefficient can be 0.63,which are better than those of the first place in the competition, and thus prove the algorithm's effectiveness and progressiveness.

Recommended Citation

Xin, Deng; Long, Can; Mi, Jianxun; Zhang, Boxian; Sun, Kaiwei; and Jin, Wang (2020) "EEG Classification Based on Multi-domain Features and Random Subspace Ensemble," Journal of System Simulation: Vol. 32: Iss. 9, Article 18.
DOI: 10.16182/j.issn1004731x.joss.19-0038
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss9/18

First Page

1787

Revised Date

2019-12-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0038

Last Page

1798

CLC

TP391.9

Recommended Citation

Deng Xin, Long Can, Mi Jianxun, Zhang Boxian, Sun Kaiwei, Wang Jin. EEG Classification Based on Multi-domain Features and Random Subspace Ensemble[J]. Journal of System Simulation, 2020, 32(9): 1787-1798.

Corresponding Author

Jianxun Mi,

DOI

10.16182/j.issn1004731x.joss.19-0038