Empirical Mode Decomposition (EMD) provides an adaptive signal processing tool, and its multivariate extension is useful to model multichannel signals. Recently, EMD and multivariate EMD have successfully been applied to solve different signal processing problems. Electroencephalogram signals are often employed to explore the emotional concepts for human-machine interaction. In this paper, an emotion recognition model is presented via EEG signal decomposition by utilizing multivariate EMD. Intrinsic Mode Functions extracted by the multivariate EMD algorithm are quasi-orthogonal. Hence the Gram-Schmidt Orthogonalization method is applied to the extracted IMFs. The number of orthogonal components reveals the number of modes used in the second step of the proposed method, where the Empirical Wavelet Transform is used to explore different features of the IMFs. By applying Ensemble and Decision Tree classifiers on the calculated features, the emotional states are classified as high-low arousal, valence, and dominance with 72.7%, 62.0%, and 64.7% highest classification performances using the selected channels, respectively.

经验模式分解 (EMD) 提供了一种自适应信号处理工具，其多元扩展对于多通道信号建模非常有用。最近，EMD 和多元 EMD 已成功应用于解决不同的信号处理问题。脑电图信号通常用于探索人机交互的情感概念。本文利用多元 EMD 通过脑电信号分解提出了一种情绪识别模型。由多元 EMD 算法提取的本征模态函数是准正交的。因此，Gram-Schmidt 正交化方法应用于提取的 IMF。正交分量的数量揭示了该方法第二步中使用的模式数量，其中经验小波变换用于探索 IMF 的不同特征。通过对计算出的特征应用集成和决策树分类器，情绪状态被分类为高-低唤醒度、效价和主导度，使用所选通道的最高分类性能分别为 72.7%、62.0% 和 64.7%。