Comparative study of multi-headed and baseline deep learning models for ADHD classification from EEG signals.

The prevalence of Attention-Deficit/Hyperactivity Disorder among children is rising, emphasizing the need for early and accurate diagnostic methods to address associated academic and behavioral challenges. Electroencephalography-based analysis has emerged as a promising noninvasive approach for detecting Attention-Deficit/Hyperactivity Disorder; however, utilizing the full range of electroencephalography channels often results in high computational complexity and an increased risk of model overfitting. This study presents a comparative investigation between a proposed multi-headed deep learning framework and a traditional baseline single-model approach for classifying Attention-Deficit/Hyperactivity Disorder using electroencephalography signals. Electroencephalography data were collected from 79 participants (42 healthy adults and 37 diagnosed with Attention-Deficit/Hyperactivity Disorder) across four cognitive states: resting with eyes open, resting with eyes closed, performing cognitive tasks, and listening to omniarmonic sounds. To reduce complexity, signals from only five strategically selected electroencephalography channels were used. The multi-headed approach employed parallel deep learning branches-comprising combinations of Bidirectional Long Short-Term Memory, Long Short-Term Memory, and Gated Recurrent Unit architectures-to capture inter-channel relationships and extract richer temporal features. Comparative analysis revealed that the combination of Long Short-Term Memory and Bidirectional Long Short-Term Memory within the multi-headed framework achieved the highest classification accuracy of 89.87%, significantly outperforming all baseline configurations. These results demonstrate the effectiveness of integrating multiple deep learning architectures and highlight the potential of multi-headed models for enhancing electroencephalography-based Attention-Deficit/Hyperactivity Disorder diagnosis.