As an advanced function of human brain, emotion has a great impact on people's personality characteristics and mental health. By using the online Deap database, emotions are divided according to psychological valence and arousal level, and the two emotions of stress and calm are studied and analyzed. On the basis of using db4 wavelet decomposition and reconstruction algorithm to decompose the signal, according to the characteristics of the asymmetry of EEG signals in the generation of emotions, a new method of emotional feature extraction is proposed, By dividing the differential entropy of right leads by the difference between left and right symmetrical electrodes, and dividing the differential entropy of right leads by the sum of the differential entropy of left and right symmetrical electrodes, the asymmetric entropy characteristics of EEG signals is extracted. Using the support vector machine optimized by genetic algorithm for emotion classification recognition, the average recognition rate is 88.625％.Comparing with the classification recognition rate of traditional features, the classification recognition rate using the asymmetric entropy feature is significantly improved.%情绪作为人脑的高级功能,对人们的个性特征和心理健康有很大的影响,利用网上公开的脑电情绪数据库(Deap数据库),根据心理较价和激励唤醒度等级进行情绪划分,对压力和平静两种情绪进行研究分析.在利用db4小波分解与重构算法分解信号的基础上,根据左右脑脑电在产生情绪时脑电信号非对称性的特点,提出一种新的情感特征提取方法,通过计算右侧导联的微分熵值除以左、右对称导联的微分熵之差与右侧导联的微分熵值除以左、右对称导联的微分熵之和,提取出脑电信号的不对称熵特征.利用遗传算法优化的支持向量机对情绪分类识别,平均识别率为88.625％,对比传统特征的分类识别率,利用不对称熵特征的分类识别率有明显提高.
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