Diagnosing Autism Spectrum Disorders Based on EEG Analysis: a Survey

Abstract

Autism spectrum disorders (ASDs) are pervasive neurodevelopmental conditions characterized by impairments in reciprocal social interactions, communication skills, and stereotyped behavior. Since EEG recording and analysis is one of the fundamental tools in diagnosing and identifying disorders in neurophysiology, researchers strive to use the EEG signals for diagnosing individuals with ASD. We found that studies on ASD diagnosis using EEG techniques could be divided into two groups in which where analysis was based on either comparison techniques or pattern recognition techniques. In this paper, we try to explain these two sets of algorithms along with their applied methods and results. Lastly, evaluation measures of diagnosis algorithms are discussed.