Exploring the potential of EEG and interpretable AI models for ADHD detection in children.
In the context of diagnosing Attention Deficit Hyperactivity Disorder (ADHD) in children, this study explores a range of machine learning and deep learning techniques. Our primary objective is to utilize brain signals for early diagnosis. The emphasis is placed on developing transparent and interpretable artificial intelligence models to enhance the efficiency of diagnosis and intervention. We conducted an intensive experimental study on a public dataset with the aim of evaluating the effectiveness of various techniques using sophisticated performance metrics.
The project aims to develop an innovative web/mobile application for the diagnosis and support of children with ADHD.
This application offers an interactive, engaging, and effective approach to assess the child's condition while providing stimulating concentration games.
