Engineering a Timing-Accurate VR-EEG-Oculomotor Fusion System for Objective Assessment of Executive Function in ADHD

Objective, scalable assessment of executive function in Attention-Deficit/Hyperactivity Disorder (ADHD) is limited by subjective rating scales and single modality tests. We present a timing-characterized virtual-reality (VR) implementation of the Wisconsin Card Sorting Test (WCST) synchronized with EEG and oculomotor/cephalic sensing for objective ADHD assessment in children. Stimulus-onset timing was empirically validated with a dedicated photodiode measurement across screen locations and distractor conditions. One hundred and one children (7-15 years; 64 ADHD, 37 control) completed a tutorial and a WCST session with distraction and no-distraction blocks. Task performance, head rotation, eye movement, and EEG features including N2/P3 event related potentials (ERP) were extracted, and group and condition contrasts on the ERP time-courses were evaluated using non-parametric cluster-based permutation testing. Classification used eight machine learning algorithms and a deep neural network (DNN) under 5-fold cross-validation; the best early-fusion DNN (task performance + head rotation + eye movement + N2/P3) reached 83% accuracy, and the best ERP-only model reached 79% under distraction. Overall, a timing characterized VR-EEG oculomotor fusion system captures ADHD-related executive function differences through complementary multimodal integration, providing a scalable, objective adjunct to clinical ADHD screening with empirically quantified stimulus-timing performance.