Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

ABSTRACT: Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive utility of multimodal data, including eye tracking, EEG, actigraphy, and behavioral indices, in differentiating adults with ADHD from healthy individuals. Using a support vector machine model, we analyzed independent training (n = 50) and test (n = 36) samples from two clinically controlled studies. In both studies, participants performed an attention task (continuous performance task) in a virtual reality seminar room while encountering virtual distractions. Task performance, head movements, gaze behavior, EEG, and current self-reported inattent

SUBMITTER: Wiebe A 

PROVIDER: S-EPMC11688437 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature