Dual-Stage Improvement with Domain Adaptation for Cross-Subject Epileptic Seizure Prediction

This study proposes a machine learning method using domain adaptation to improve EEG-based seizure prediction across different patients, addressing challenges of data imbalance and noisy labels. The approach combines generative networks to balance preictal/interictal samples with optimized spatial filtering and pseudo-label refinement techniques.