Artificial Intelligence #rector#masked modeling
RECTOR Framework Sets New State-of-the-Art in EEG Emotion Recognition and sEEG Classification
Researchers propose RECTOR, a self-supervised framework for representation learning from EEG/sEEG data, achieving state-of-the-art performance in emotion recognition and task-engagement classification. The model demonstrates strong robustness to missing channels and cross-montage generalization, promising for large-scale pre-training on heterogeneous neural data.
Jun 16, 2026 1 source