Band-Specific Graph Learning for EEG-Based Emotion Recognition

doi:10.1109/LSP.2026.3699586

Band-Specific Graph Learning for EEG-Based Emotion Recognition

2026-06-02

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DOI：10.1109/LSP.2026.3699586

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Electroencephalogram (EEG)-based emotion recognition has attracted increasing attention due to the high temporal resolution of EEG signals. However, accurate EEG-based emotion recognition remains challenging because EEG signals inherently exhibit low signal-to-noise ratios, non-stationarity and considerable inter-individual variability. In addition, brain functional connectivity presents distinct band-dependent characteristics, whereas many existing graph-based methods employ shared or static graphs across spectral bands, limiting their ability to characterize band-specific inter-channel interactions. To address these issues, we propose BSGNet, a band-specific graph learning framework for EEG-based emotion recognition. Specifically, instead of assuming a unified connectivity structure, BSGNet learns a set of band-specific channel adjacency matrices to capture functional interactions associated with different spectral bands. The learned graphs are further integrated into a STGCN backbone to jointly capture spatial dependencies among EEG channels and temporal dynamics of emotional responses. Experimental results on two public EEG emotion recognition datasets show that BSGNet achieves improved average performance over representative deep learning and graph-based baselines, supporting the effectiveness of band-specific connectivity modeling for EEG representation learning.

Keywords:

Brain–computer interface

electroencephalography

emotion recognition

graph convolutional neural networks

Band-Specific Graph Learning for EEG-Based Emotion Recognition

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