Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontocentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontocentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain.

Electroencephalogram (EEG) methods for the diagnosis of Benign Epilepsy with Centrotemporal Spikes (BECTS) are reviewed. The focus is on procedures reported for EEG analysis and diagnosis in BECTS, since some recent and potential applications of artificial intelligence (AI) aim to enhance the diagnostic accuracy and time reduction process, thereby moving a step closer to advancing our knowledge of the electrical nuclei sources and dynamics of energy distribution through the scalp in patients with epilepsy. The advantages of AI classification techniques have an increasing publication rate in the specialist literature, with no clear agreement on methodology. Hence, a better understanding of the procedures, arguments, and achievements is needed. To achieve this goal, (1) we review the background knowledge of the clinical characteristics of BECTS, (2) we analyze the results and advantages of computational processing methods for source and connectivity analyses of EEG in BECTS, and finally, (3) we explore the AI methods published in specialized journals for BECTS analysis. In conclusion, we argue in favor of the combined use of a priori information, which is the basis of the clinical visual analysis of EEG, as a potential feature to be included in AI methods for the classification of epileptiform graphoelements in EEG in BECTS diagnosis.