Enhanced EEG Complexity and Spectral Alterations in Migraine: A PSD and Higuchi's Fractal Dimension Analysis by ICA and PCA Spatial Filters

Migraine is a complex neurological disorder associated with abnormal functional connectivity and altered neural dynamics. This study investigates cortical complexity and spectral properties in episodic migraine using EEG-derived resting-state networks (RSNs) analysed with power spectral density (PSD) and Higuchi's fractal dimension (HFD). EEG data from 22 migraineurs and 25 healthy controls were processed using Independent Component Analysis (ICA) and Principal Component Analysis (PCA) spatial filters to extract functionally relevant brain networks. PSD analysis revealed increased low frequency (delta, theta) and decreased high frequency (beta, gamma) power in migraineurs, suggesting disturbed oscillatory activity. HFD analysis showed significantly higher complexity in several RSNs, indicating irregular neural dynamics. Notably, HFD scores correlated with the age of migraine onset and duration, highlighting potential effects on disease progression. These findings suggest widespread dysregulation of brain network dynamics in migraine and support EEG complexity measures as potential biomarkers for diagnosis and treatment monitoring.