FreqDGT packages frequency weighting, dynamic graphs, and adversarial disentanglement into one EEG emotion model, but reports higher accuracy using a binary classification protocol that is not comparable to the multi-class baselines it cites.
Approaches, applications, and challenges in physiological emotion recognition—a tutorial overview
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FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition
FreqDGT packages frequency weighting, dynamic graphs, and adversarial disentanglement into one EEG emotion model, but reports higher accuracy using a binary classification protocol that is not comparable to the multi-class baselines it cites.