Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network

EEG based emotion recognition technology is currently an important concept in artificial intelligence, and also holds great potential in emotional health care. Nevertheless, one major limitation of the prior approaches is they do not capture the relationships between different time-series and channels explicitly, resulting in inevitable low performance, especially in subject-independent recognition settings. In this paper, we propose a novel graph attention network based model to address this issue. Our framework includes dual-pipeline Graph Attention Network layers in parallel to learn the complex dependencies of multi-channel EEG in both temporal and spatial dimensions. The proposed method outperforms other state-of-the-art models on benchmark SEED dataset. Further analysis shows that our method also has good interpretability. As far as we know, it is the first work that introduce graph attention network into EEG based emotion detection research.