Common Spatial Pattern Using Multivariate EMD for EEG Classification
computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most e.ective ways. This paper presents a method for classifying EEG during motor-imagery by the combination of well-known common spatial pattern (CSP) with so-called multivariate empirical mode decomposition (MEMD), which is e.ectively suitable for processing of multichannel signals of EEG. In the proposed method, the EEG signal is decomposed into intrinsic mode functions (IMF) using the MEMD. Di.erent from EMD, the number of IMF is the same in each channel. Then by removing some of the IMFs, the reconstructed signal can carry more useful information than the original signal. Based on the MEMD, weights of CSP are found. By o.-line simulation, the use of MEMD in CSP has shown to perform well in the application to the classification of EEG signals.
Long Zhang Cheng Zhang Hiroshi Higashi Jianting Cao Toshihisa Tanaka
Tokyo University of Agriculture and Technology, Japan Tokyo University of Agriculture and Technology, Japan RIKEN Brain Science Institute, Japan Saitama I Saitama Institute of Technology, Japan RIKEN Brain Science Institute, Japan
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-5
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）