Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.

最近，用脑电图（EEG）信号识别情绪已受到越来越多的关注。此外，大脑的非平稳性加剧了非线性方法的应用。尽管如此，诸如二次样本熵（QSE），幅度感知置换熵（AAPE）和置换最小熵（PME）之类的指标从未用于识别两种以上的情绪。因此，本研究首次计算了QSE，AAPE和PME以识别四组情绪。在预处理脑电图记录后，计算了三个熵度量。然后，实现了一种基于顺序前向选择方案和支持向量机分类器的十分类方法。此程序被应用到一个多类计划中，该计划同时包括四组学习，并采用二元类的方法来区分情绪的唤醒和效价水平，从而一二三辨。对于这两种方案，将QSE + AAPE和QSE + PME结合在一起。在多分类和二分类分类方案中，在额叶和顶叶脑区域均获得最佳结果。此外，在大多数情况下，从分类模型中选择了QSE和AAPE / PME的通道，从而突出了这些不同类型的熵指标之间的互补性，并在多类和二类方案中获得了高于90％的全局精度结果。基于正则性和可预测性的熵指数的组合表示这些非线性方法之间的高度互补性。最后，