LG-GAT: Local-Global Graph Attention Network for EEG Emotion Recognition

Emotion recognition has become a focal research area in brain-computer interfaces, aiming to leverage artificial intelligence for diagnosing and treating clinical disorders. Neuropsychological studies indicate a close relationship between the activities of different brain functional areas and emotions. Consequently, we introduce a Local-Global Graph Attention Network (LG-GAT), a model inspired by neurology that learns the intra-regional and inter-regional activities of the brain by studying the local-global graph representations of electroencephalography (EEG). The LG-GAT mainly consists of two modules, namely the temporal convolution layer and kernel-level attention fusion (KAF) module, the feature graph building (LTG) module. The KAF module is to learn specific task-related features from EEG signals and to fuse the features learned from different kernels. The LTG module utilizes graph attention networks combined with local and global graphical representations with neurophysiological significance to model complex relationships within and between functional areas of the brain. To demonstrate LG-GAT’s efficacy, it was evaluated on three public datasets: DEAP, DREAMER, and SEED. Furthermore, LG-GAT’s performance was compared with state-of-the-art (SOTA) methods. The results indicate that LG-GAT surpasses these methods, validating the enhancement of emotion recognition performance through the integration of neuroscientific prior knowledge into neural network design.