Chao Hao, Cao Yiming, Liu Yongli
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.
Front Neurosci. 2023 Jul 25;17:1135850. doi: 10.3389/fnins.2023.1135850. eCollection 2023.
In this paper, a novel EEG emotion recognition method based on residual graph attention neural network is proposed. The method constructs a three-dimensional sparse feature matrix according to the relative position of electrode channels, and inputs it into the residual network to extract high-level abstract features containing electrode spatial position information. At the same time, the adjacency matrix representing the connection relationship of electrode channels is constructed, and the time-domain features of multi-channel EEG are modeled using graph. Then, the graph attention neural network is utilized to learn the intrinsic connection relationship between EEG channels located in different brain regions from the adjacency matrix and the constructed graph structure data. Finally, the high-level abstract features extracted from the two networks are fused to judge the emotional state. The experiment is carried out on DEAP data set. The experimental results show that the spatial domain information of electrode channels and the intrinsic connection relationship between different channels contain salient information related to emotional state, and the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.
本文提出了一种基于残差图注意力神经网络的新型脑电情感识别方法。该方法根据电极通道的相对位置构建三维稀疏特征矩阵，并将其输入到残差网络中以提取包含电极空间位置信息的高级抽象特征。同时，构建表示电极通道连接关系的邻接矩阵，并使用图对多通道脑电的时域特征进行建模。然后，利用图注意力神经网络从邻接矩阵和构建的图结构数据中学习位于不同脑区的脑电通道之间的内在连接关系。最后，将从两个网络中提取的高级抽象特征进行融合以判断情感状态。在DEAP数据集上进行了实验。实验结果表明，电极通道的空间域信息以及不同通道之间的内在连接关系包含与情感状态相关的显著信息，且所提出的模型能够有效地融合这些信息以提高多通道脑电情感识别的性能。
