Electroencephalography Representation Learning with Spatiotemporal Reconstruction for Accurate Attention Deficit Hyperactivity Disorder Detection

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by persistent patterns of inattention, hyperactivity, and impulsivity that interfere with daily functioning or development. Traditional diagnosis of ADHD often relies on self-report checklists and behavioral assessments, which can be subjective and prone to bias. These methods may overlook subtle neural patterns that are indicative of ADHD which leads to misdiagnosis or delayed intervention. To address these limitations, I propose an Electroencephalography (EEG)-based ADHD screening system utilizing machine learning algorithms. The proposed network takes a set of EEG signals as input and outputs the probability that an individual has ADHD. To enhance accuracy, I introduce a novel EEG representation learning technique to capture the spatiotemporal features of EEG data. The trained network learns to extract rich features from the EEG signals, which significantly improves the accuracy of ADHD detection. Through extensive experiments, the proposed system achieved state-of-the-art performance in screening ADHD, reaching an accuracy of 99%. Additionally, further experiments were conducted to identify which parts of the brain are highly correlated with the screening of ADHD. In conclusion, this research not only validates the effectiveness of the EEG-based system but also contributes to our understanding of ADHD's neurobiological basis.