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

Mental Health Monitoring And Intervention Using Unsupervised Deep Learning On EEG Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mental Health Monitoring And Intervention Using Unsupervised Deep Learning On EEG Data Akhila Reddy Yadulla, Guna Sekhar Sajja, Santosh Reddy Addula, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014270/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Unsupervised Deep Learning Gaussian Mixture Model (GMM) K-Means EEG-Signals (Electroencephalography) Generative Adversarial Networks (GAN) Mental Health Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5014270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350949578,"identity":"828fe09d-5dfa-498c-9bf8-a136627fe73d","order_by":0,"name":"Akhila Reddy Yadulla","email":"","orcid":"","institution":"University of the Cumberlands","correspondingAuthor":false,"prefix":"","firstName":"Akhila","middleName":"Reddy","lastName":"Yadulla","suffix":""},{"id":350949579,"identity":"df84be11-668d-435a-9a66-a5c79bab4701","order_by":1,"name":"Guna Sekhar Sajja","email":"","orcid":"","institution":"University of the 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