Local-Global Feature Fusion for Subject-Independent EEG Emotion Recognition

• URL: http://arxiv.org/abs/2601.08094v1
• Date: Tue, 13 Jan 2026 00:27:01 GMT
• Title: Local-Global Feature Fusion for Subject-Independent EEG Emotion Recognition
• Authors: Zheng Zhou, Isabella McEvoy, Camilo E. Valderrama,
• Abstract summary: We propose a fusion framework that integrates (i) local, channel-wise descriptors and (ii) global, trial-level descriptors.<n>Experiments under a leave-one-subject-out protocol demonstrate that the proposed method consistently outperforms single-view and classical baselines.
• Score: 5.248014945077116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subject-independent EEG emotion recognition is challenged by pronounced inter-subject variability and the difficulty of learning robust representations from short, noisy recordings. To address this, we propose a fusion framework that integrates (i) local, channel-wise descriptors and (ii) global, trial-level descriptors, improving cross-subject generalization on the SEED-VII dataset. Local representations are formed per channel by concatenating differential entropy with graph-theoretic features, while global representations summarize time-domain, spectral, and complexity characteristics at the trial level. These representations are fused in a dual-branch transformer with attention-based fusion and domain-adversarial regularization, with samples filtered by an intensity threshold. Experiments under a leave-one-subject-out protocol demonstrate that the proposed method consistently outperforms single-view and classical baselines, achieving approximately 40% mean accuracy in 7-class subject-independent emotion recognition. The code has been released at https://github.com/Danielz-z/LGF-EEG-Emotion.

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