Pretraining a region-aware spatiotemporal EEG encoder with JEPA generative and masked dynamic contrastive losses on FACED yields higher cross-subject accuracy than SSL baselines when fine-tuned on SEED-IV/V/VII.
FCAnet: A novel feature fusion approach to EEG emotion recognition based on cross-attention networks,
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Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
Pretraining a region-aware spatiotemporal EEG encoder with JEPA generative and masked dynamic contrastive losses on FACED yields higher cross-subject accuracy than SSL baselines when fine-tuned on SEED-IV/V/VII.