基于共同空间模式的情感脑电信号的空域特征提取

Extraction of spatial features of emotional EEG signals based on common spatial pattern

为了改善基于脑电(EEG)的情感分类性能,提高多分类情况下的识别准确率,提出了一种基于共同空间模式(CSP)的空域滤波算法。首先使用传统的CSP方法设计空域滤波器,并通过该滤波器对3种情感类型(即积极、中性和消极)的EEG信号进行线性投影,以提取空域特征。此外,考虑到传统近似联合对角化(JAD)算法是使用"得分最高的特征值"准则进行特征向量的选择,该情况可能导致无法有效区分多分类的情感状态,因此针对最高分特征值位置存在的所有可能情况设计了不同的特征值选择方法。对实验室自主采集数据集,使用支持向量模型(SVM)作为分类器进行对比实验。结果表明基于CSP的空域特征提取方法在三分类情感识别中平均准确率达到了87.54%,证明其在情感识别应用中具有可行性。

In order to enhance the performance of electroencephalogram(EEG)-based emotion recognition and improve the accuracy of multi-classification, a spatial filtering algorithm using the common spatial pattern(CSP) was proposed. Firstly, the traditional CSP method was used to design the spatial domain filter. On this basis, three types of emotion recognition EEG signals(i.e., positive, neutral, and negative) were linearly projected by this filter, so as to extract spatial features. Furthermore, considering that the traditional joint approximation diagonalization(JAD) algorithm using the "highest score eigenvalue" criterion may result in the failure to distinguish the multi-classification emotional states, different eigenvalue selection methods were designed in terms of the position of the eigenvalues with the highest scores. Under our lab environment, the comparative experiments using the support vector model(SVM) as a classifier have been carried out. The results show that the CSP-based spatial feature extraction method has an impressive accuracy of87.54% on average in three-class emotion state recognition, proving the feasibility of the method in the application of emotion recognition.