关键词: 脑电波, 共空间模式, 小波包分解, 情感分类

Abstract: To effectively alleviate the effect of Electroencephalogram（EEG） differences on classification performance in different subjects during cross-day sessions, this paper suggests to combine Common Spatial Pattern（CSP） and Wavelet Packet Decomposition（WPD） algorithm to make filtering preprocessing and feature extraction on EEG data of 12 subjects for 5 consecutive days. The Bagging tree, SVM, LDA and BLDA models are used for the binary（happy and sad） emotional classification experiments. The experimental results show that the best classification accuracy of SVM and BLDA models on two types of EEG features extracted by CSP and WPD algorithms is respectively 4.4% and 3.5% higher than the state-of-the-art, which indicates that the proposed method can effectively improve the accuracy and stability of cross-day EEG emotion classification and has certain value for the development of robust affective brain-computer interface applications.

Key words: Electroencephalogram（EEG）, common spatial patterns, wavelet packet decomposition, emotion classification