Large-Scale Synchronization Dynamics During Epileptic Seizures: A Patient-Independent EEG Network Analysis

This study examines large-scale synchronization dynamics during epileptic seizures using scalp EEG recordings, with the aim of characterizing reproducible network-level patterns across patients. Functional connectivity was estimated from the CHB-MIT database using phase-lag-based measures robust to volume conduction, specifically Imaginary Coherence and the debiased weighted phase lag index, across standard frequency bands. Synchronization features were used to train a neural network classifier evaluated under a Leave-One-Patient-Out (LOPO) validation framework to ensure patient-independent assessment. To quantify seizure-related network alterations, we introduce Relative Pathological Synchronization (RPS), defined as the median area under the ROC curve across patients. The results demonstrate that synchronization patterns deviate systematically from baseline activity in a time-dependent manner. Interhemispheric connectivity shows earlier and higher peak RPS values compared to intrahemispheric connectivity, while intrahemispheric changes develop more gradually and persist over a longer interval. Theta-band features provide the most consistent contribution, although interhemispheric synchronization involves multiple frequency bands. In addition, longer seizures are associated with higher peak RPS values. These findings indicate that large-scale synchronization patterns contain stable, patient-independent information about seizure dynamics. Specifically, interhemispheric connectivity achieved a peak RPS of 0.749 (0.609–0.891) at TAS=10 s, while intrahemispheric connectivity reached 0.640 (0.563–0.843) at TAS=30 s under strict Leave-One-Patient-Out validation.