Revolutionizing ADHD Diagnosis: Harnessing ResNet for Enhanced EEG Analysis

This study investigates the integration of neurofeedback and digital cognitive interventions alongside traditional therapeutic approaches for managing Attention Deficit Hyperactivity Disorder (ADHD). Leveraging electroencephalogram (EEG) data, the research combines conventional machine learning and advanced deep learning techniques to analyze brain activity patterns indicative of ADHD. The study meticulously assesses the efficacy of these innovative therapies, using a dataset with over one million EEG entries, to enhance diagnostic precision and optimize treatment outcomes. The classification report from the study shows exemplary performance metrics: precision and recall rates near perfection (0.99 and 1.00 respectively for the negative class, 1.00 and 0.99 for the positive class) and F1-scores consistently high at 0.99 for both classes, demonstrating the potential of these methods in clinical settings. This comparative analysis evaluates the potential of integrated neurofeedback and digital interventions against the backdrop of traditional pharmacological and behavioral treatments.