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

The objective of this project is to enhance the accuracy of EEG signal classification for diagnosing neurological disorders, including addictive disorders, anxiety disorders, mood disorders, obsessive-compulsive disorder (OCD), schizophrenia, and trauma-related stress disorders. The project aims to leverage advanced machine learning and deep learning models such as XGBoost, Random Forest, and Deep Learning techniques to improve the reliability of EEG signal classification. By categorizing EEG signals into the distinct classes mentioned above, the goal is to provide healthcare professionals with more accurate, early diagnosis tools, ultimately aiding in better treatment and care management for patients with neurological conditions.