A Multiple Autocorrelation Analysis Method for Motor Imagery EEG Feature Extraction
A novel multiple autocorrelation method for single trial EEG feature extraction was proposed.The time courses of ERD/ERS during motor imagery were investigated by calculating multiple autocorrelation before power spectrum analysis.Then the averaged power spectrums on specific frequency bands were sent to a K-nearest classifier to validate the separability between different classes.Compared with the result of power spectrum,the multiple autocorrelation performed better in attenuating noise and enhancing the separability between different classes with a small quantity of electrodes(C3 and C4).The maximum 90.0%accuracy tested on dataset of BCI-competition 2003 for motor imagery is achieved.
motor imagery multiple autocorrelation BCI signal separability
Xiangzhou Wang An Wang Shuhua Zheng Yingzi Lin Mingxin Yu
School of Automation,Beijing Institute of Technology,Beijing 100081 Mechanical and Industrial Engineering Department,Northeastern University,Boston,MA 02115,USA
国际会议
长沙
英文
3000-3004
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）