关键词: 脑-机接口, 特征提取, 小波包分解, 组合分类器, 投票组合

Abstract: In order to improve classification accuracy, this paper describes a novel method based on Wavelet Packet Decomposition（WPD） and voting combination of multiple classifiers to classify the Motor Imagery（MI） Electroencephalogram（EEG） signals. First, the pre-processed MI data is decomposed into wavelet coefficients using WPD, from which features based on Relative Wavelet Packet Energy（RWPE） in all sub-bands are extracted; then, the L-2 norm of wavelet packet coefficients in special sub-bands at channels C3 and C4 is obtained according to the diversity of the hemispheric brainwave in different mental tasks; finally, features are combined to feed into a multiple classifier combination based on majority voting strategy for classification and correct rate of 92.85% is achieved. The experimental results indicate that the RWPE and L-2 norm effectively reflect the event-related desynchronization and synchronization（ERD and ERS） characteristics of left and right MI；the combined classifier improves the classification performance, which is superior to the single classifier.

Key words: Brain-Computer Interface（BCI）, feature extraction, Wavelet Packet Decomposition（WPD）, multiple classifiers, voting combination