BALLADEER ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder affecting children and adolescents, characterized by inattention, hyperactivity, and impulsivity. Current diagnostic methods primarily rely on subjective clinical evaluations, which are prone to bias. Advances in neurophysiological assessment, particularly through electroencephalography (EEG), eye tracking, and electrodermal activity (EDA), offer promising avenues for objective diagnosis and monitoring of ADHD. However, the lack of large, publicly available multimodal datasets has hindered progress in machine/deep learning-based ADHD classification and biomarker discovery. To address this gap, we present the BALLADEER ADHD Dataset, a comprehensive, multimodal dataset integrating EEG, eye tracking, and physiological signals from children and adolescents with ADHD and neurotypical controls. The dataset was collected by using a controlled experimental protocol involving cognitive and attentional tasks, including Attention Slackline, Attention Robots, and CogniFit. These tasks were designed to elicit neurophysiological responses associated with attentional control, response inhibition, and cognitive flexibility—key domains affected in ADHD. This dataset enables the development and validation of machine/deep learning models for ADHD classification and fosters research into digital biomarkers for objective assessment. Additionally, it supports cross-modal analyses, exploring the interplay between EEG dynamics, eye movements, and autonomic nervous system activity in attentional performance. We detail our methodology for data acquisition, preprocessing, and quality assurance, highlighting potential applications in clinical and research settings. By making this dataset publicly available, we aim to enhance transparency, reproducibility, and innovation in computational neuroscience and ADHD research.