EEG-Based Effective Connectivity Analysis for Attention Deficit Hyperactivity Disorder Detection Using Color-Coded Granger-Causality Images and Custom Convolutional Neural Network

Abstract

Background: Attention deficit hyperactivity disorder (ADHD) is prevalent worldwide, affecting approximately 8-12% of children. Early detection and effective treatment of ADHD are crucial for improving academic, social, and emotional outcomes. Despite numerous studies on ADHD detection, existing models still lack accuracy distinguishing between ADHD and healthy control (HC) children. Methods: This study introduces an innovative methodology that utilizes granger causality (GC), a well-established brain connectivity analysis technique, to reduce the required EEG electrodes. We computed GC indexes (GCI) for the entire brain and specific brain regions, known as regional GCI, across different frequency bands. Subsequently, these GCIs were transformed into color-coded images and fed into a custom-developed 11-layer convolutional neural network. Results: The proposed model is evaluated through a five-fold cross-validation, achieving the highest accuracy of 99.80% in the gamma frequency band for the entire brain and an accuracy of 98.50% in distinguishing the theta frequency band of the right hemisphere of ADHD and HC children by only using eight electrodes. Conclusion: The proposed framework provides a powerful automated tool for accurately classifying ADHD and HC children. The study’s outcome demonstrates that the innovative proposed methodology utilizing GCI and a custom-developed convolutional neural network can significantly improve ADHD detection accuracy, improving affected children’s overall quality of life.