MVGT: A Multi-view Graph Transformer Based on Spatial Relations for EEG Emotion Recognition

TL;DR

This paper introduces MVGT, a multi-view graph transformer that integrates temporal, frequency, and spatial information from EEG signals, significantly improving emotion recognition accuracy.

Contribution

The paper presents a novel multi-view graph transformer model that effectively captures multi-domain EEG features for emotion recognition, outperforming existing methods.

Findings

MVGT achieves higher accuracy than state-of-the-art methods.

The model effectively captures multi-domain EEG features.

Enhanced modeling of inter-channel relationships improves performance.

Abstract

Electroencephalography (EEG), a technique that records electrical activity from the scalp using electrodes, plays a vital role in affective computing. However, fully utilizing the multi-domain characteristics of EEG signals remains a significant challenge. Traditional single-perspective analyses often fail to capture the complex interplay of temporal, frequency, and spatial dimensions in EEG data. To address this, we introduce a multi-view graph transformer (MVGT) based on spatial relations that integrates information across three domains: temporal dynamics from continuous series, frequency features extracted from frequency bands, and inter-channel relationships captured through several spatial encodings. This comprehensive approach allows model to capture the nuanced properties inherent in EEG signals, enhancing its flexibility and representational power. Evaluation on publicly available datasets demonstrates that MVGT surpasses state-of-the-art methods in performance. The results highlight its ability to extract multi-domain information and effectively model inter-channel relationships, showcasing its potential for EEG-based emotion recognition tasks.