Contrastive learning based multi-scale spatio-temporal-spectral network for patient-specific EEG seizure prediction

Accurate seizure prediction using electroencephalogram (EEG) signals is crucial for improving patients’ quality of life. However, the latent representations of preictal and interictal samples are difficult to effectively distinguish within the feature space. In this study, we propose a patient-specific seizure prediction framework that combines contrastive pre-training with multi-scale spatio-temporal-spectral EEG modeling. Instead of relying on cross-patient transfer, the proposed contrastive strategy regularizes the intra-patient latent space by enhancing the consistency of seizure-related representations before supervised fine-tuning. A multi-scale encoder is further designed to capture temporal dynamics, frequency-specific neural rhythms, and graph attention-guided spatial dependencies among EEG channels. Window-level predictions are finally converted into event-level alarms through a post-processing strategy. Experiments on the CHB-MIT and Siena datasets demonstrate that the proposed method achieves superior performance compared with representative baselines, with an average AUC of 0.942, sensitivity of 94.61%, and FPR/h of 0.034 on CHB-MIT, and an AUC of 0.935, sensitivity of 92.34%, and FPR/h of 0.090 on Siena. These results suggest that the proposed method can improve both discriminability and alarm reliability in seizure prediction.