Elastic Graph Transformer Networks for EEG-based Emotion Recognition

Electroencephalogram (EEG) has been applied in emotion recognition due to excellent temporal resolution with less competitive spatial resolution. This leads to the consequence that the majority of EEG-based emotion recognition models emphasize on exploiting temporal features while ignoring the efficient information provided by spatial resolution. To extract more informative representations, we propose an elastic Graph Transformer network for emotion recognition (EmoGT) inspired by the advantages of Transformer in time-series analysis and the superior performance of graph convolutional networks in topological analysis. Moreover, it is able to be flexibly expanded to cope with multimodal inputs by employing specially designed structures. Experimental results on 3 public datasets demonstrate that our models outperform the state-of-the-art results by 3% on average in both single and multimodal cases, indicating the effectiveness of utilizing temporal and spatial information simultaneously.