In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.

中文翻译：

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使用动态图卷积神经网络进行 EEG 情绪识别

在本文中，提出了一种基于新型动态图卷积神经网络（DGCNN）的多通道脑电情感识别方法。所提出的脑电情感识别方法的基本思想是使用图对多通道脑电特征进行建模，然后基于该模型进行脑电情感分类。与传统的图卷积神经网络 (GCNN) 方法不同，本文提出的 DGCNN 方法可以通过训练神经网络动态学习不同脑电图 (EEG) 通道之间的内在关系，以邻接矩阵表示，从而有利于更具判别力的脑电图特征提取。然后，学习到的邻接矩阵用于学习更多的判别特征，以提高 EEG 情绪识别。我们对上海交通大学情感 EEG 数据集 (SEED) 和 DREAMER 数据集进行了大量实验。实验结果表明，所提出的方法比最先进的方法实现了更好的识别性能，其中主题相关实验的平均识别准确率为 90.4%，而主题独立交叉验证的平均识别准确率为 79.95%。 SEED 数据库中，DREAMER 数据库对效价、唤醒和优势分类的平均准确率分别为 86.23%、84.54% 和 85.02%。