An explainable machine learning model based on multidimensional task-state electroencephalography for identifying...

Attention-deficit/hyperactivity disorder (ADHD) and developmental dyslexia (DD) are highly comorbid, yet objective differentiation from individual disorders remains challenging due to the lack of neurophysiological biomarkers. This study aimed to develop an interpretable machine learning model leveraging multidimensional task-state EEG features to distinguish ADHD-DD comorbidity from ADHD and typical development (TD).

Ninety children (27 ADHD-DD, 40 ADHD, 23 TD) underwent EEG during a VSWM task. Five feature categories were extracted: event-related potentials, time-frequency characteristics, functional connectivity, graph-theoretic metrics, and nonlinear dynamics. Three feature-processing strategies and nine classifiers were compared within a nested cross-validation framework. Feature contribution was quantified by coefficient back-projection of the optimal PCA-LR mode. Sensitivity and permutation analyses were performed.

The PCA-LR model achieved a balanced accuracy of 80.5%, a Macro-F1 of 0.799, and a Macro-AUC of 0.912. Class-wise AUCs were 0.905 for ADHD-DD, 0.904 for ADHD, and 0.927 for TD. Left temporal α-band functional connectivity was a shared high-contribution feature in both disorder groups. ADHD-DD was distinguished by widely distributed cross-regional connectivity spanning θ and α bands and by P200 amplitude. ADHD was characterized primarily by α-band network metrics and α-ERD, whereas TD relied mainly on θ and α bands functional connectivity and graph-theoretic metrics.

Our findings suggest that multidimensional task-state EEG features may help differentiate children with ADHD-DD comorbidity from those with ADHD or typical development. They also indicate that machine-learning approaches may be useful for identifying neurodevelopmental comorbid conditions.