Multiscale deep learning convolutional neural network for ADHD detection using EEG

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition in children. Electroencephalography (EEG) may support objective assessment, but subject-independent ADHD classification remains challenging because EEG varies across participants and recording segments. We developed the EEG Multiscale Convolutional Network (EEG-MSCNet), a multiscale convolutional architecture for pediatric EEG classification, and evaluated it using subject-level leave-one-subject-out (LOSO) validation on a public EEG dataset. The study also examined preprocessing choices, architectural ablations, channel-region subsets, computational complexity, and repeated-run stability. The final EEG-MSCNet evaluation achieved an Accuracy of 87.6%, F1-score of 87.6%, and area under the receiver operating characteristic curve (AUROC) of 88.2%. Artifact subspace reconstruction (ASR) combined with Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER) emerged as the strongest preprocessing configuration. The multiscale architecture showed stronger observed Accuracy and F1-score than reduced architectural variants, and the full montage provided the strongest channel configuration. These findings show that subject-level validation, preprocessing, and architectural decisions materially influence EEG-based ADHD classification. EEG-MSCNet contributes both a model and a systematic evaluation framework, supported by source code, configurations, logs, and the public dataset reference for reproducible comparison.