🤖 AI Summary
To address the challenge of jointly modeling temporal, spectral, and spatial features in EEG-based emotion recognition, this paper proposes a spatial-relation-aware multi-view Graph Transformer architecture. We introduce a novel three-view graph attention mechanism that simultaneously captures temporal dynamics, band-specific spectral responses, and multi-scale spatial relationships—including Euclidean distance, topological adjacency, and functional connectivity. By integrating graph neural networks with multi-head self-attention, our model unifies the representation of complex inter-channel dependencies and cross-domain interactions. Evaluated on multiple benchmark EEG emotion datasets, the proposed method consistently outperforms state-of-the-art approaches, achieving average classification accuracy improvements of 2.3–4.1%. These results validate the effectiveness of joint multi-domain feature representation and structured channel-relational modeling. Our work establishes a new paradigm for interpretable and robust EEG-based emotion decoding.
📝 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.