Zhang Jing, Zhang Xueying, Chen Guijun, Zhao Qing
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Brain Sci. 2022 Dec 1;12(12):1649. doi: 10.3390/brainsci12121649.
Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal-valence classifications, respectively.
图卷积神经网络（GCN）在脑电图（EEG）情感识别任务中备受关注。然而，当前GCN的大多数特征在构建邻接矩阵的过程中并未充分利用不同频带EEG信号之间的因果关系。基于格兰杰因果关系（GC）分析获得的EEG通道间的因果连通性，本文提出了一种用于EEG情感识别的多频带EEG图特征提取与融合方法。首先，通过GC分析计算每个频带EEG信号之间的原始GC矩阵，然后通过最优阈值将它们自适应转换为非对称二元GC矩阵。接着，分别以微分熵特征和二元GC矩阵作为节点值和邻接矩阵，构建了一种新型的基于GC的GCN特征（GC-GCN）。最后，在GC-GCN特征的基础上，提出了一种新的多频带特征融合方法（GC-F-GCN），该方法整合了同一节点不同频带EEG信号的图信息。实验结果表明，所提出的GC-F-GCN方法比当前最先进的GCN方法具有更好的识别性能，在唤醒度、效价和唤醒度-效价分类中分别达到了97.91%、98.46%和98.15%的平均准确率。
