Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders

EEG signal classification is an important area of research for diagnosing and understanding various neurological disorders. As the prevalence of disorders such as anxiety, mood disorders, obsessive-compulsive disorder (OCD), schizophrenia, and trauma-related stress increases, the ability to accurately classify EEG signals becomes essential for early detection and effective treatment. This study focuses on the use of machine learning and deep learning techniques to classify EEG signals into distinct categories: Addictive disorder, Anxiety disorder, Healthy control, Mood disorder, Obsessive-compulsive disorder, Schizophrenia, and Trauma and stress-related disorder.

We investigate the application of advanced algorithms such as XGBoost, Random Forest, and Deep Learning models, specifically designed to analyze and identify patterns in EEG signals. These techniques aim to distinguish between the various conditions and provide insights into the underlying neural mechanisms. Through this research, we assess the effectiveness of each model in terms of accuracy, precision, recall, and robustness for neurological disorder classification.

The results of this study underscore the potential of machine learning and deep learning methods in providing accurate and timely classification of EEG signals, ultimately supporting healthcare professionals in diagnosing neurological disorders more effectively. This research contributes to the advancement of AI-powered medical diagnostics, with the potential to revolutionize how neurological conditions are diagnosed and treated.

Keywords: EEG Signal Classification, XGBoost, Random Forest, Deep Learning, Machine Learning, Neurological Disorders, Anxiety, Mood Disorders, Obsessive-Compulsive Disorder, Schizophrenia, Trauma-Related Disorders, AI in Healthcare Diagnostics.