Migraine analysis through EEG signals with classification approach

Migraine is a common type of headache with neurovascular origin. In this paper, a quantitative analysis of spontaneous EEG patterns is used to examine the migraine patients with maximum and minimum pain levels. The analysis is based on alpha band phase synchronization algorithm. The efficiency of extracted features are examined through one-way ANOVA test. we reached the P-value of 0.0001, proving that the EEG patterns are statistically discriminant in maximum and minimum pain levels. We also used a Neural Network based approach in order to classify the EEG patterns, distinguishing between minimum and maximum pain levels. We achieved the total accuracy of 90.9 %.