ADHD-Conformer: EEG-Based Classification and Detection of ADHD

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders among children, often leading to cognitive, emotional, and social impairments. Traditional diagnostic methods rely heavily on behavioral observation and subjective questionnaires, lacking objective physiological indicators. This study proposes a novel brain-computer interface (BCI) assisted diagnostic method based on EEG signal classification using a deep learning hybrid model named ADHD-Conformer. The model combines convolutional neural networks (CNNs) for local feature extraction and transformer-based architectures for capturing global temporal dependencies. Experiments on open-source datasets demonstrate that our approach achieves superior classification performance, with accuracy reaching 99.01%, thus proving the feasibility of deep learning-assisted ADHD diagnosis.