Electroencephalogram (EEG) emotion recognition based on a hybrid feature extraction method in empirical mode decomposition domain combining with optimal feature selection based on sequence backward selection is proposed, which can reflect subtle information of multiscale components of unstable and nonlinear EEG signals and remove the reductant features to improve the performance of emotion recognition. The proposal is tested on DEAP dataset, in which the emotional states in the Valance dimension and Arousal dimension are classified by both ${K}$ -nearest neighbor and support vector machine, respectively. In the experiments, temporal windows of different length and three kinds of rhythms of EEG signal are taken into account for comparison, from which the results show that EEG signal with 1s temporal window achieves highest recognition accuracy of 86.46% in Valence dimension and 84.90% in Arousal dimension, respectively, which is superior to some state-of-the-art works. The proposed method would be applied to real-time emotion recognition in multimodal emotional communication-based humans–robots interaction system.

中文翻译：

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基于经验模式分解和最优特征选择的脑电情绪识别

提出了一种基于经验模式分解域的混合特征提取方法结合基于序列后向选择的最优特征选择的脑电(EEG)情感识别，能够反映不稳定和非线性脑电信号多尺度分量的细微信息，去除还原特征。以提高情绪识别的性能。该提议在 DEAP 数据集上进行了测试，其中 Valance 维度和 Arousal 维度中的情绪状态分别由 ${K}$ -最近邻和支持向量机分类。实验中考虑了不同长度的时间窗和脑电信号的三种节律进行比较，从中结果表明，具有1s时间窗口的EEG信号在Valence维度和Arousal维度分别达到86.46%和84.90%的最高识别准确率，优于一些最先进的作品。所提出的方法将应用于基于多模态情感交流的人机交互系统中的实时情感识别。