Improving ADHD Diagnosis with Real-Time EEG Artifact Correction and Emotional Insights

‐ Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often subjective, posing challenges to accuracy. This study enhances diagnosis by integrating real-time EEG artifact correction and emotional insights. EEG, a non-invasive neuroimaging tool, detects brain activity patterns, such as abnormal theta-to-beta ratios, commonly linked to ADHD. However, EEG signals are prone to artifacts from muscle movements and external noise. To address this, Independent Component Analysis (ICA) and machine learning algorithms are employed for real-time artifact correction. In addition, emotion recognition through EEG provides insights into emotional dysregulation, often seen in ADHD. Results show significant improvements in EEG signal quality, emotional state classification, and diagnostic accuracy, achieving 90% accuracy. These findings highlight the potential of combining neural and emotional data to enhance ADHD diagnosis and foster personalized treatment strategies targeting both cognitive and emotional components of the disorder.