This paper proposes an electroencephalogram(EEG) emotion recognition method based on the LIBSVM classifier. EEG features are calculated to represent the characterisitics associated with emotion states. First calculating the Lempel–Ziv complexity and wavelet detail coefficients for the pre-processed EEG signals; then obtaining the co-integration relationship that reflecting the relationship between channels according to the cointegration test; next performing Empirical Mode Decomposition(EMD) on the pre-processed EEG signals; finally calculating the average approximate entropy of the first four Intrinsic Mode Functions(IMFs). The calculated four features are input into the LIBSVM classifier to realize the sentiment classification of each channel data, and then the classification results of each channel are fused by the Takagi–Sugeno fuzzy model to achieve the final emotion classification. The experimental results show that when the two-category classifications are performed on Arousal and Valance, the average sentiment recognition rates are 74.88% and 82.63%, respectively.

提出了一种基于LIBSVM分类器的脑电图情感识别方法。计算脑电图特征以表示与情绪状态相关的特征。首先计算预处理的脑电信号的Lempel-Ziv复杂度和小波细节系数；然后根据协整检验获得反映通道间关系的协整关系。接下来对预处理的脑电信号进行经验模态分解（EMD）；最终计算出前四个本征模函数（IMF）的平均近似熵。计算出的四个特征输入到LIBSVM分类器中，以实现每个通道数据的情感分类，然后通过Takagi–Sugeno模糊模型融合每个通道的分类结果，以实现最终的情感分类。实验结果表明，对“情感”和“价”进行两类分类时，平均情感识别率分别为74.88％和82.63％。