Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. So, designing accurate and reliable prediction methods can provide early warning for patients, which is crucial for improving their lives. In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. Important time-varying information may be overlooked if only graph space features during seizures are considered. To address these issues, we propose a synchronization-based graph spatio-temporal attention network (SGSTAN). This model effectively leverages the intricate information embedded within EEG recordings through spatio-temporal correlations. Experimental results on public datasets demonstrate the efficacy of our approach. On the CHB-MIT dataset, our method achieves accuracy, specificity, and sensitivity scores of 98.2%, 98.07%, and 97.85%, respectively. In the case of challenging subjects that are difficult to classify, we achieved an outstanding average classification accuracy of 97.59%, surpassing the results of previous studies.
