关键词: 脑电信号, 共空间模式（CSP）, 偏最小二乘回归（PLS）, 数据增强

Abstract: When the principal component analysis（PCA） algorithm reduces the dimensionality of the EEG signal features extracted from the common spatial pattern（CSP）, it only considers the representation ability of the principal component to the input variables, and ignores the interpretation of the output variables. To overcome this shortcoming, this paper proposes a dimensionality reduction method of partial least squares regression（PLS）. Firstly, CSP extracts the feature of signal dealt with data augmentation, and then PLS performs dimensionality reduction on the extracted features. In particular, the paper takes the features that includes a higher level interpretation for the dependent variable in principal component as feature vectors. Finally, PSO-SVM is used to classify. The proposed algorithm is applied on the data set IIIa of the 2005 BCI competition, and the average classification accuracy rate of the three subjects’ imagination movement is 91.71%. The classification result has reached the highest average classification accuracy rate in comparison with algorithms such as CSP-LDS, WL-CSP and CSP, which verifies the effectiveness of the algorithm.

Key words: electroencephalogram（EEG）, common spatial pattern（CSP）, partial least squares regression（PLS）, data augmentation