Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction.