EEG-Based ADHD Diagnosis in Children Using Channel Attention and Lightweight Deep Learning

In this study, EEGNet-CA, a lightweight deep learning model with an integrated attention mechanism, is proposed to diagnose Attention Deficit and Hyperactivity Disorder (ADHD) in children via EEG data. The proposed model is able to learn the contribution of each EEG channel in the classification and focus on important signals by integrating the channel attention (CA) mechanism into the classical EEGNet architecture. In this context, 19-channel EEG recordings from 121 children aged 7–12 years, obtained from the IEEE dataset, were used. The raw EEG signals were scaled with z-score normalization to improve the stability of the learning process and segmented using the 50 % overlap sliding window method. The EEGNet-CA model, developed using the obtained EEG segments, was utilised for classification purposes. Experimental results show that the proposed EEGNet-CA model achieved a 99.79 % F1-score and exhibited statistically significantly superior performance compared to standard EEGNet, 1D CNN, 2D CNN, and artificial neural network-based methods. In addition, high accuracy even with only frontal region channels supports the neurophysiological importance of this region in ADHD diagnosis. The results show that the CA mechanism is an effective method to increase the classification success in EEG-based diagnostic systems.