EEG Analysis in Benign Epilepsy with Centro-Temporal Spikes: A Comprehensive Review

The electroencephalogram (EEG) methods of analysis for diagnosis of Benign Epilepsy with Centrotemporal Spikes (BECTS) are reviewed. The focus is called on the proce-dures reported in scientific literature for EEG analysis and diagnosis in BECTS because some recent and potential applications of artificial intelligence (AI) aiming to enhance the diagnostic accuracy and time reduction process, support to be the next step for ad-vancing our knowledge of the electrical nuclei sources and dynamics of energy distri-bution through scalp in patients suffering epilepsy. The advantages of AI classification techniques have an increasing publication rate in specialist literature, with no clear agreement in methodology. Hence, a better comprehension of procedures, arguments and achievements is needed. For reaching this goal, we review 1) the background knowledge of clinical characteristics of BECTS; 2) analyze the results and advantages of computational processing methods for source and connectivity analysis of EEG in BECTS; finally, 3) we explore the IA methods published in specialized journals for BECTS analysis. In conclusion, we argue in favor of a combined use of a priori infor-mation that is the base of the clinical visual analysis of EEG as a potential feature to be included in AI methods for classification of epileptiform graphoelements in EEG in BECTS diagnosis.