Long-Term EEG-Based Modeling and Classification of Migraine Phases Using Hidden Markov Models

Abstract

Migraine is a complex neurological disorder characterized by dynamic alterations in brain activity during multiple phases: interictal (baseline), preictal, ictal, and postictal. This study aims to model and differentiate these migraine phases using electroencephalogram (EEG) and a Hidden Markov Model (HMM). EEG signals were collected from each subject over several months through frequent, short sessions—often multiple times per day. The recordings were temporally aligned with self-reported symptom diaries, allowing for precise labeling of migraine phases. A comprehensive set of features was extracted from the EEG signals, including spectral, temporal, and nonlinear measures—such as Dynamic Mode Decomposition (DMD) and Katz Fractal Dimension (KFD)—across various frequency bands. Despite the limited number of participants, the dense long-term recordings captured multiple migraine episodes, enabling reliable phase modeling. The HMM identified distinguishable neural patterns corresponding to migraine states, suggesting the feasibility of temporal EEG modeling for clinical applications in personalized migraine management.