Optimal channel selection of electroencephalography based on functional network via global graph measurements: application for epilepsy.

Electroencephalography (EEG) plays a vital role in diagnosing and managing epilepsy, but the traditional technique requires numerous electrodes, making it time-consuming and burdensome for patients. To overcome this, several studies propose reducing the number of channels, often requiring patient-specific selections, which limits their generalizability. This study presents a novel network-based approach for channel selection during the preictal period. We employed 1078 preictal EEG signals collected from 33 epilepsy patients at Korea University Anam Hospital. Using these signals, we established a functional connectivity network and used Pearson's correlation coefficient to assess the correlation between the selected channels' network features and the full network's features. Our findings revealed that a network composed of eight channels, four in each hemisphere located in the midline frontal and parietal regions, exhibited a high correlation with the entire network. Encouragingly, applying the method to the publicly available Siena database yielded similar results. These findings indicate that selecting channels in the midline frontal and parietal regions during the preictal phase effectively preserves the entire network's features. This suggests that our proposed channel reduction approach could not only enhance our understanding of various brain network-related diseases but also improve patient comfort and efficiency in clinical settings.