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Abstract：
Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell\'s circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51-100Hz) of EEG signals rather than low frequency oscillations (0.3-49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.
摘要：
探索情绪在脑电图信号中的表现形式有助于提高情绪识别的准确性。本文介绍了基于Russell'scenterplex模型的基于EEG信号多尺度信息分析（MIA）的新特征，用于在四个维度上区分情绪状态。这些算法被应用于DEAP数据库上的特征提取，其中包括时域中的多尺度脑电复杂性指数，集成经验模态分解增强了频域中的能量和模糊熵。采用支持向量机和交叉验证方法对分类精度进行评估。MIA方法的分类性能(准确率=62.01%，精度=62.03%，召回率/敏感度=60.51%，特异性=82.80%)远高于经典方法(准确率=43.98%，精度=43.81%,召回率/敏感度=41.86%，和特异性=70.50%)，基于离散小波变换提取的特征包含相似能量，分形维数,和样本熵。在这项研究中,我们发现，情绪识别与EEG信号的高频振荡（51-100Hz）而不是低频振荡（0.3-49Hz）更相关，额叶和颞叶区域的重要性高于其他区域。这样的信息具有预测能力并且可以提供对分析EEG信号中的高频振荡的多尺度信息的更多见解。
