Abstract：
Objective.Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels.Approach.In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity.Main results.Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively.Significance.Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.
摘要：
目标：尽管情感识别已经研究了几十年，仍然需要一种更准确的分类方法，需要更少的计算。目前,在许多研究中,从所有通道中提取EEG特征以识别情绪状态，然而,缺乏提高分类性能并减少EEG通道数量的有效特征域。
方法：在本研究中，提出了一种基于连续小波变换（CWT）的多通道EEG数据特征表示方法，用于自动情感识别。在建议的功能中，通过使用CWT系数来保留时频域信息。对于特定的EEG通道，每个CWT系数被映射成强度与熵分量比,以获得2D表示。最后,2D特征矩阵，即CEF2D，是通过连接来自不同通道的这些表示来创建的，并将其馈送到深度卷积神经网络架构中。基于CWT域能量熵比，还提出了有效的信道和CWT尺度选择方案以降低计算复杂度。
结果：与以前的研究相比，这项研究的结果表明，在3类和2类病例中，效价和唤醒分类的准确性都有所提高。对于2类问题，效价和唤醒维度的平均准确度为98.83%和98.95%，分别,对于三等舱，准确率分别为98.25%和98.68%,分别。
结论：我们的发现表明，CWT域中基于熵的EEG数据特征对于情绪识别是有效的。利用建议的特征域，一种有效的信道选择方法可以降低计算复杂度。
方法：在本研究中，提出了一种基于连续小波变换（CWT）的多通道EEG数据特征表示方法，用于自动情感识别。在建议的功能中，通过使用CWT系数来保留时频域信息。对于特定的EEG通道，每个CWT系数被映射成强度与熵分量比,以获得2D表示。最后,2D特征矩阵，即CEF2D，是通过连接来自不同通道的这些表示来创建的，并将其馈送到深度卷积神经网络架构中。基于CWT域能量熵比，还提出了有效的信道和CWT尺度选择方案以降低计算复杂度。
结果：与以前的研究相比，这项研究的结果表明，在3类和2类病例中，效价和唤醒分类的准确性都有所提高。对于2类问题，效价和唤醒维度的平均准确度为98.83%和98.95%，分别,对于三等舱，准确率分别为98.25%和98.68%,分别。
结论：我们的发现表明，CWT域中基于熵的EEG数据特征对于情绪识别是有效的。利用建议的特征域，一种有效的信道选择方法可以降低计算复杂度。
