Evaluating the Efficacy of EEG Features and Data Fusion in Migraine Diagnosis

This study investigates the potential of using EEG features for the diagnosis of migraine. The asymmetry, phase synchronization, and relative power spectra of EEG signals were analyzed in 10 migraineurs and 10 healthy subjects. The objective of the study is to ascertain the relative importance of distinct characteristics in migraine diagnosis and establish a more effective classifier model for a limited dataset application. Singular features displayed inadequate accuracy in migraine classification under no photic stimulation, with an accuracy of roughly 70%. However, data fusion using the Random Forest algorithm resulted in a 5% increase in accuracy, achieving an accuracy of 75% with no photic stimulation and 88% under 3Hz photic stimulation. Results indicates that Random Forest is the most efficient classifier model for the identification of migraine utilizing a small dataset with numerous characteristics. The study presents innovative perspectives into the efficacy of amalgamating multiple well-known EEG abnormalities for migraine diagnosis and highlights the significance of meticulous deliberation of the choice of classification model and the scale of the dataset in constructing precise and dependable diagnostic tools for migraine. The findings demonstrate the potential for future studies to explore additional characteristics and algorithms to boost the accuracy of migraine classification. This investigation contributes to the burgeoning collection of research in the field of migraine diagnosis and presents a foundation for further inquiry into the amalgamation of multiple characteristics and the effectiveness of distinct methods.