A Trend on Autism Spectrum Disorder Research: Eye Tracking-EEG Correlative Analytics

Abstract

The greatest challenge in autism spectrum disorder (ASD) detection and treatment arises from the elusive etiological origins and heterogeneity of ASD. ET-EEG correlative analytics refers to correlating simultaneously recorded eye tracking (ET) and electroencephalography (EEG) data or combining them to yield diagnostic biomarkers. This approach was recently applied in ASD research as EEG and video-based ET are suitable for children and do not interfere with each other. It allows researchers to associate neural correlates with gaze patterns in the same cognitive task. Besides, correlative analytics has shed light on the inconsistent findings derived from the unimodal approach, reveals the developmental trajectory of ASD, and provides biomarkers to assist ASD diagnosis or intervention. This review focuses on eight articles applying ET-EEG correlative analytics and synthesized the reported correlation between ET and EEG patterns. Studies concerning such patterns that acquired either ET or EEG data were also reviewed to provide comparisons. In recent years, correlative analytics has also been developed in other research fields, such as visual information processing and EEG artifact correction. Incorporating methodologies used in these research works into ASD studies would help identify cognitive alteration related to a specific visual pattern in naturalistic settings. The opportunities and limitations of this approach in ASD research are discussed at last.