Emotional brain-computer interface based on electroencephalogram (EEG) is a hot issue in the field of human-computer interaction, and is also an important part of the field of emotional computing. Among them, the recognition of EEG induced by emotion is a key problem. Firstly, the preprocessed EEG is decomposed by tunable-Q wavelet transform. Secondly, the sample entropy, second-order differential mean, normalized second-order differential mean, and Hjorth parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf optimization algorithm is used to optimize the feature matrix. Finally, support vector machine is used to train the classifier. The five types of emotion signal samples of 32 subjects in the database for emotion analysis using physiological signal dataset is identified by the proposed algorithm. After 6-fold cross-validation, the maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and the Kappa coefficient is 0.603. The results show that the proposed method has good performance indicators in the recognition of multiple types of EEG emotion signals, and has a better performance improvement compared with the traditional methods.

中文翻译：

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通过可调 Q 因子小波变换和二元灰狼优化使用脑电图识别情绪。

基于脑电图（EEG）的情感脑机接口是人机交互领域的热点问题，也是情感计算领域的重要组成部分。其中，情绪诱发的脑电图识别是一个关键问题。首先，预处理后的脑电图通过可调Q小波变换进行分解。其次，提取每个子带的样本熵、二阶微分均值、归一化二阶微分均值和Hjorth参数（迁移率和复杂度）。然后使用二值灰狼优化算法对特征矩阵进行优化。最后，使用支持向量机训练分类器。利用该算法对数据库中32名受试者的5类情绪信号样本进行识别，利用生理信号数据集进行情绪分析。经过6折交叉验证，最大识别准确率为90.48%，敏感性为70.25%，特异性为82.01%，Kappa系数为0.603。结果表明，所提方法在多种类型脑电情感信号的识别中具有良好的性能指标，与传统方法相比有更好的性能提升。