Attention-driven deep learning framework for EEG analysis in ADHD detection.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects cognitive functions such as attention, impulse control, and executive functioning. Electroencephalography (EEG) has been widely explored as a noninvasive method for identifying abnormal brain activity patterns associated with ADHD. This study proposes an to enhance the accuracy of ADHD detection using EEG signals. The model integrates to selectively focus on critical EEG features, improving classification performance. The dataset, sourced from IEEE DataPort, includes EEG recordings from children diagnosed with ADHD and a control group. The proposed model achieves with an and , outperforming existing machine learning models such as . The results indicate that the , making it a promising tool for clinical and educational applications.