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.
García-Aguilar et al. (Thu,) studied this question.