EEG-GCN: Spatio-Temporal and Self-Adaptive Graph Convolutional Networks for Single and Multi-View EEG-Based Emotion Recognition
EEG-GCN: Spatio-Temporal and Self-Adaptive Graph Convolutional Networks for Single and Multi-View EEG-Based Emotion Recognition
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DOI：
10.1109/lsp.2022.3179946
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发表时间：
2022-01-01
影响因子：
3.9
通讯作者：
Wang, Yi
中科院分区：
文献类型：
--
作者：
Gao, Yue;Fu, Xiangling;Wang, Yi
图网络自然适合于对脑电信号的多通道特征进行建模。然而，现有的研究，试图利用基于图的神经网络的EEG为基础的情感识别没有考虑到的时空冗余的EEG特征和大脑拓扑结构的差异。在本文中，我们提出了EEG-GCN，采用时空和自适应图卷积网络的单视图和多视图基于EEG的情感识别的范例。采用时空注意机制，EEG-GCN可以自适应地捕获脑电信号中的重要序列片段和空间位置信息。同时，设计了一个自适应的脑网络邻接矩阵来量化通道间的连接强度，以表征不同情绪情景下的激活模式。此外，我们提出了一种基于多视角脑电信号的情感识别方法，该方法有效地整合了脑电信号的各种特征。在两个基准数据集SEED和DEAP上进行的大量实验表明，我们提出的方法在单视图和多视图下的性能优于其他代表性方法。
Graph networks are naturally suitable for modeling multi-channel features of EEG signals. However, the existing study that attempts to utilize graph-based neural networks for EEG-based emotion recognition doesn't take the spatio-temporal redundancy of EEG features and differences in brain topology into account. In this paper, we propose EEG-GCN, a paradigm that adopts spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition. With spatio-temporal attention mechanism employed, EEG-GCN can adaptively capture significant sequential segments and spatial location information in EEG signals. Meanwhile, a self-adaptive brain network adjacency matrix is designed to quantify the connection strength between the channels, in which way to represent the diverse activation patterns under different emotion scenarios. Additionally, we propose a multi-view EEG-based emotion recognition method, which effectively integrates the diverse features of EEG signals. Extensive experiments conducted on two benchmark datasets SEED and DEAP demonstrate that our proposed method outperforms other representative methods from both single and multiple views.