Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Abstract

ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

“…Gaze estimation, as a technique to estimate the points of gaze, can be used in a large variety of medical applications. Since it can reflect individual attention and abundant physiological information of the subjects, gaze estimation is of great potential for disability assistance [1] , Parkinson’s disease diagnosis [2] , ASD diagnosis [3] , [4] , and ADHD diagnosis [5] , [6] , [7] So far, varieties of gaze estimation methods have been proposed, ranging from scleral search coils, video-oculography (VOG) to EOG. The VOG-based technique is the most popular approach to estimating gaze due to its high performance in terms of accuracy.…”

A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking

“…Despite wide adoption, these measures are geared towards single-user studies and are thus challenging to scale for multi-user studies. To elaborate, since eye trackers are designed to capture eye movements of one individual at a time, eye-tracking studies are often carried out as single-user experiments (Jayawardena et al 2021b;Mahanama et al 2022c;Michalek et al 2019;Senarath et al 2022) in isolated environments (Mahanama 2022a(Mahanama , 2021. Moreover, these measures only capture individual-level behaviors and do not account for inter-individual interactions.…”