P300 feature extraction with wavelet packet transform and CSSD - 文章详情页

作者：

Li, M.-A. (Li, M.-A..) (学者：李明爱) | Li, X. (Li, X..) | Yang, J.-F. (Yang, J.-F..) (学者：杨金福) | Hao, D.-M. (Hao, D.-M..)

收录：

Scopus PKU CSCD

摘要：

P300　potential　is　weak　and　has　poor　anti-interference　ability　and　low　recognition　rate.　Based　on　wavelet　packet　transform　(WPT)　and　common　spatial　subspace　decomposition　(CSSD),　a　feature　extraction　method,　denoted　as　WPCSSD,　was　proposed　in　this　paper.　First,　the　EEG　was　preprocessed　by　the　overlapping　average　algorithm　to　improve　its　signal-to-noise　ratio.　Second,　the　EEG　was　filtered　and　reconstructed　by　WPT　according　to　the　time-frequency　information　of　P300.　Third,　the　power　spectrum　based　on　AR　model　was　computed,　and　a　spatial　filter　with　CSSD　was　applied　to　extract　the　spatial　feature　of　P300.　The　feature　vector　can　therefore　reflect　the　time-frequency-space　information　of　P300　generally.　Finally,　the　support　vector　machine　was　used　for　classification.　Results　show　that　WPCSSD　has　better　anti-interference　and　adaptive　ability,　and　the　recognition　accuracy　is　95.22%　in　data　sets　of　BCI　competition.　The　correctness　and　validity　of　the　method　are　proven.

关键词：

AR model power spectrum; Common spatial subspace decomposition (CSSD); Feature extraction; P300 potential; Wavelet packet

作者机构：

[ 1 ] [Li, M.-A.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Li, X.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Yang, J.-F.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 4 ] [Hao, D.-M.]College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, China

通讯作者信息：

李明爱
[Li, M.-A.]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

电子邮件地址：

limingai@bjut.edu.cn

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