Research Paper#EEG, Emotion Recognition, Domain Adaptation, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:42
EEG-based Domain Adaptation for Cross-Session Emotion Recognition
Analysis
This paper addresses the challenge of cross-session variability in EEG-based emotion recognition, a crucial problem for reliable human-machine interaction. The proposed EGDA framework offers a novel approach by aligning global and class-specific distributions while preserving EEG data structure via graph regularization. The results on the SEED-IV dataset demonstrate improved accuracy compared to baselines, highlighting the potential of the method. The identification of key frequency bands and brain regions further contributes to the understanding of emotion recognition.
Key Takeaways
- •Addresses the challenge of cross-session variability in EEG-based emotion recognition.
- •Proposes the EGDA framework for domain adaptation.
- •Achieves improved accuracy on the SEED-IV dataset.
- •Identifies key frequency bands and brain regions for emotion recognition.
Reference
“EGDA achieves robust cross-session performance, obtaining accuracies of 81.22%, 80.15%, and 83.27% across three transfer tasks, and surpassing several baseline methods.”