EEG Decoding Based on Wavelet Transform and Common Space Pattern

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EEG Decoding Based on Wavelet Transform and Common Space Pattern

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[1]QU Silin,WANG Congqing,LI Jianliang,et al.EEG Decoding Based on Wavelet Transform and Common Space Pattern[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):31-36.[doi:10.13705/j.issn.1671-6833.2021.06.003]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 03 Page number: 31-36 Column: Public date: 2022-04-10

Title:: EEG Decoding Based on Wavelet Transform and Common Space Pattern

Author(s):: QU Silin¹; 2; WANG Congqing¹; 2; LI Jianliang¹; 2; ZHAN Wenhao²; ZHANG Min¹; 1.School of Automation,Nanjing University of Aeronautics and Astronautics, Nanjing 210016，China;
2.National Key Laboratory of Human Factors Engineering, China Astronauts Research and Training Center, Beijing 100094，China

Keywords:: BCI; EEG decoding; long short term memory network; space manipulator; CSP

CLC:: TP391. 4

DOI:: 10.13705/j.issn.1671-6833.2021.06.003

Abstract:: Aiming at the problem of fewer tasks and low accuracy of recognition for motion imagination electroencephalogram(EEG)signals, in this paper a common space pattern(CSP)method based on wavelet packet decomposition(WPD)was proposed to extract the features of EEG signals.The long short term memory network was used to decode the EEG signals.Motor imagination signals were separated from blind sources by independent component analysis(ICA), and each channel EEG signal was divided into 8 groups by frequency using wavelet packet decomposition.The power value of each group of signals was calculated, 10 features were removed for classification by recurrence feature elimination(RFE).The selected signals were extracted by one-to-one common space pattern filters.The feature matrix were input into a long shortterm memory network for EEG decoding, and the classification results of 4 categories of motion imagination signals were obtained.The proposed method was used to verify the open data set of brain computer interface(BCI)competition(including four kinds of EEG signals: left hand imagination signal, right hand imagination signal, tongue imagination signal, and foot imagination signal).The recognition accuracy of three subjects was 90.28%, 94.25% and 96.55% respectively, and the average recognition accuracy could reach 93.69%.Compared with other feature extraction and classification methods, this method had a high classification accuracy.The decoded EEG signals were used to control the clockwise or counterclockwise movement of the space manipulator to achieve the purpose of grasping the target in the virtual space environment.

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Last Update: 2022-05-02