Explainable CNN-based ADHD detection using EEG data

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition marked by persistent symptoms of inattention, hyperactivity, and impulsivity, significantly affecting individuals across all age groups globally. Accurate and timely diagnosis is critical for effective intervention, yet current diagnostic methods often rely on subjective clinical evaluations and behavioral assessments, which can be inconsistent and prone to bias. To address these challenges, this study introduces an innovative data-driven approach for the automated detection of ADHD using Electroencephalography (EEG) data, leveraging Convolutional Neural Network (CNN) models integrated with explainability techniques.The proposed methodology employs advanced preprocessing techniques to extract meaningful features from raw EEG signals, capturing subtle neural activity patterns associated with ADHD. Utilizing a hybrid dataset comprising EEG recordings from both children and adults, the model demonstrates robust performance, achieving an accuracy of 98.91% on unseen test data. These results underscore the model's potential for precise and reliable ADHD detection, offering a significant improvement over traditional diagnostic methods. To ensure transparency and interpretability in clinical applications, two state-of-the-art explainability techniques—Local Interpretable Model-agnostic Explanations (LIME) and SHAPley Additive Explanations (SHAP)—were employed. LIME approximates the model's behavior for specific data instances, identifying influential features in individual predictions, while SHAP provides a global perspective by quantifying feature importance across the dataset. These techniques validated the relevance of specific EEG channels and features in distinguishing ADHD, revealing critical biomarkers and enhancing model interpretability. This study establishes a comprehensive framework for automated ADHD detection, integrating deep learning with robust explainability methods to ensure accuracy and transparency. By bridging the gap between advanced machine learning techniques and clinical applicability, this work promotes objective, early, and reliable ADHD diagnosis. Beyond ADHD detection, the framework's adaptability suggests potential extensions to other neurodevelopmental disorders, highlighting its broader implications in AI-driven healthcare solutions.M.S.Includes bibliographical reference