Feature extraction of EEG high order tensor based on EEMD
FU Rong-rong1,YANG Yang1,YU Bao1,LIU Chong2,ZHANG Chi3
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. College of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning 110819,China
3. School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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.
付荣荣,杨阳,于宝,刘冲,张驰. 基于集合经验模态分解的脑电信号高阶张量特征提取[J]. 计量学报, 2021, 42(12): 1679-1685.
FU Rong-rong,YANG Yang,YU Bao,LIU Chong,ZHANG Chi. Feature extraction of EEG high order tensor based on EEMD. Acta Metrologica Sinica, 2021, 42(12): 1679-1685.
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2021, Vol. 42 
