Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification

Our method achieved exceptional performance in EEG analysis tasks. Our method consistently outperformed state-of-the-art approaches across TUSZ, TUAB, and TUEV datasets, achieving the highest AUROC (0.891), balanced accuracy (0.8002), weighted F1-score (0.8038), and Cohen's kappa (0.6089). These results validate its robustness, generalization, and effectiveness in seizure detection and classification tasks on diverse EEG datasets.