Spatiotemporal Gated Graph Transformer for EEG-Based Emotion Recognition

The availability of accurate and reliable electroencephalography (EEG) signal data makes emotion recognition feasible. In recent years, an increasing number of deep learning methods have been applied to emotion recognition tasks based on EEG signals, but they all have their own drawbacks, including the inability to simultaneously capture spatial and temporal information, the loss of spatial information, and even the improper selection of data scales. To address the above problems, this paper proposes a novel method for EEG-based emotion recognition named Spatiotemporal Gated Graph Transformer (SGGT). The method takes advantage of graph structure to enrich spatial information and uses a two-tower transformer to encode spatial information and temporal information, respectively. For the spatial feature extraction, we adopt four encoding methods to compensate for the shortcomings of traditional transformers in graph learning tasks. Different from the previous processing methods, our method uses graph pooling to compress the graph, forcing the model to learn the relationship between different time stamps. In addition, a gating mechanism is used to merge the two-tower transformer to realize the fusion of temporal and spatial features. Our method is validated on the SEED, SEED-IV and SEED-V datasets, and it outperforms the baseline methods.