Mental Health Monitoring And Intervention Using Unsupervised Deep Learning On EEG Data

This study explores the analysis of EEG signal data for real-time mental health monitoring using advanced unsupervised deep learning models. Employing algorithms such as autoencoders, Principal Component Analysis (PCA), K-means clustering, and Gaussian Mixture Models (GMM), this research aims to uncover patterns and biomarkers indicative of various mental health conditions. The study utilizes a comprehensive dataset comprising EEG signals from different brain regions, focusing on the extraction of significant features and the training of models to detect subtle yet crucial changes in brain activity. Our findings demonstrate enhanced capability for early detection of mental health issues, with improved predictive accuracy and potential for personalized therapy, underscoring a promising future for mental health care. Furthermore, the study rigorously addresses the ethical implications of using algorithmic approaches in healthcare, such as potential biases, patient privacy, and the welfare of individuals. By implementing these unsupervised deep learning models, our research offers compelling opportunities for the prevention, tailored intervention, and improved treatment outcomes in mental health care while also emphasizing the importance of navigating the ethical complexities to ensure responsible technology deployment for enhancing patient well-being and safety.