共空间模式结合小波包分解的脑电情感分类

为了有效缓解不同受试者跨天试验间脑电信号差异对分类性能的影响,结合共空间模式和小波包分解算法,对12个受试者连续5天的脑电波数据进行空间滤波处理和时频域上小波包能量特征提取。采用Bagging tree、SVM、LDA和BLDA模型进行情感分类实验。实验结果表明,使用SVM和BLDA分类器对该算法提取的脑电特征进行两类情感分类的精度比目前最优的结果分别提高了4.4%和3.5%,有效地提高了跨天脑电情感分类的准确率和稳定性,对于开发鲁棒的情感脑-机接口应用具有一定价值。

EEG Emotion Classification Based on Common Spatial Patterns and Wavelet Packet Decomposition

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.