Within-cohort discrimination and cross-cohort transfer of EEG-based ADHD classifiers: An exploratory two-cohort evaluation

We evaluated whether participant-separated within-cohort discrimination was retained when electroencephalography (EEG) classifiers for attention-deficit/hyperactivity disorder (ADHD) were applied to a different population and recording task. This exploratory reanalysis included 121 children performing a visual task and 79 adults recorded during eyes-open rest after basic channel screening. Both cohorts used Cz/F4, 128 Hz, and the first fifteen non-overlapping 2-second epochs per participant. Fixed logistic regression and random forest classifiers used spectral, amplitude-derived, and combined features. Internal evaluation used five repetitions of five-fold participant-grouped cross-validation; bidirectional transfer fitted transformations and classifiers only on the source cohort. Adult spectral balanced accuracy (BA) was 0.9519–0.9546, compared with 0.5504–0.5664 in children. Adult spectral logistic regression exceeded all 999 internal label permutations (Monte Carlo p=0.001; within-family Holm-adjusted p=0.002). Paired spectral-minus-amplitude conditional intervals excluded zero in adults but spanned zero in children. Cross-cohort spectral BA was 0.3584–0.4675, with area under the receiver operating characteristic curve (AUC) of 0.0309–0.3857 in the unchanged score direction. Increasing pediatric source-epoch budgets moved random-forest AUC toward 0.5, whereas logistic-regression AUC remained below 0.1. Strong adult internal discrimination thus coexisted with reverse cross-cohort ranking. These findings characterize the evaluated pipelines, not clinical diagnostic utility or a unique mechanism: age, task, and acquisition differences were not separated, target results had previously been inspected, and conditional intervals excluded model-refitting uncertainty.