基于集合经验模态分解的脑电信号高阶张量特征提取

doi:10.3969/j.issn.1000-1158.2021.12.20

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Abstract In view of the shortcomings of 2D principal component analysis (2DPCA)which ignores the frequency domain characteristics of electroencephalography (EEG) and the limitation that wavelet parameters are difficult to determine when constructing EEG high-order tensor based on wavelet decomposition, a feature extraction method based on ensemble empirical mode decomposition (EEMD)and multi linear principal component analysis (MPCA) is proposed. The contrast experiments of three different feature extraction methods are designed, and the classification accuracy is obtained by combining Fisher linear discriminant analysis classification method. The results show that compared with the feature extraction method of constructing high-order tensor based on wavelet decomposition combined with MPCA for dimensionality reduction and 2DPCA, the average recognition accuracy is improved by 4.75% and 2.6% respectively, and the variance of recognition accuracy is reduced by 72.69% and 23.86% respectively. The new feature extraction method not only improves the recognition accuracy of single motor imagery EEG signal, but also has better applicability, which lays the foundation for the realization of motor imagery EEG signal decoding.

Key words： metrology EEG;EEMD MPCA brain computer interface feature extraction

Received: 18 November 2020 Published: 06 December 2021

PACS:	TB99
	TB973

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	Articles by authors
	FU Rong-rong
	YANG Yang
	YU Bao
	LIU Chong
	ZHANG Chi

Cite this article:

FU Rong-rong,YANG Yang,YU Bao, et al. Feature extraction of EEG high order tensor based on EEMD[J]. Acta Metrologica Sinica, 2021, 42(12): 1679-1685.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.12.20 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I12/1679

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