学术文献库

Current methods for clustering brain networks over time often rely on cross-dependence measures computed from the entire range of EEG signals, which can obscure information specific to extreme neural activity. To overcome this, we introduce Club Exco, a novel clustering method grounded in extreme value theory, designed to detect brain communities with co-occurring high-amplitude EEG events. By focusing on tail behavior, Club Exco isolates extreme-value synchrony across channels, offering new insights into seizure dynamics. We apply Club Exco to neonatal EEG recordings from 30 patients (13 seizure-free and 17 with clinically confirmed seizures). Our method identifies robust ``brain extreme communities'' and constructs Extreme Connectivity Persistence matrices that summarize how often channels exhibit synchronous extremes across time. Seizure patients exhibit more persistent and variable clustering among non-adjacent regions, suggesting seizure propagation, while non-seizure patients show more consistent clustering in anatomically adjacent regions. Compared to coherence-based methods (e.g., Hierarchical Cluster Coherence procedure), Club Exco captures distinct, seizure-associated connectivity patterns, especially in high-amplitude segments. These results highlight Club Exco's potential to characterize extreme neural events and inform clinical understanding of seizure localization and spread.