Automated EEG-Based Classification of Nonclinical Depressive States via the Integration of Automatic Speech Recognition and a Pretrained Language Model

Automated EEG-Based Classification of Nonclinical Depressive States via the Integration of Automatic Speech Recognition and a Pretrained Language Model | 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 Article Automated EEG-Based Classification of Nonclinical Depressive States via the Integration of Automatic Speech Recognition and a Pretrained Language Model Hiroki Watanabe, Aya S. Ihara, Masato Okada, Sakriani Sakti, Mitsuyoshi Tachimori, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6913281/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The early detection of depressive states is crucial for the effective prevention of clinical depression. To this end, a previous method enabled the classification of nonclinical depressive states using electroencephalogram (EEG) data recorded during a daily activity, such as listening to news, for continuous daily monitoring. However, this method required the manual annotation of word onset times and emotional valence, making its real-time application impractical. Furthermore, manual feature extraction from EEG responses cannot adequately address variability caused by differences in news content and participant characteristics such as age. To overcome these limitations, this study integrates automatic speech recognition to extract word onset times and a pretrained language model-based sentiment analysis to classify the emotional valence of the news contents, thereby enabling automated annotation. In addition, a convolutional neural network-based end-to-end classification framework is proposed to account for variability in EEG responses. In practical evaluation involving 186 participants aged 22-77, including 44 individuals who self-reported a depressive state, the proposed method outperformed the previous manual method. These findings demonstrate the feasibility of classifying nonclinical depressive states using automated EEG data analysis collected during passive listening. Biological sciences/Biological techniques/Electrophysiology/Electroencephalography eeg Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Neuroscience/Cognitive neuroscience/Language Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Nov, 2025 Reviews received at journal 01 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers invited by journal 13 Aug, 2025 Editor invited by journal 23 Jun, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 17 Jun, 2025 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-6913281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":480665574,"identity":"bd5f95e9-2895-4784-bbf4-eecdcef28267","order_by":0,"name":"Hiroki Watanabe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAwSD+QBDArooTi0HwAy2BKAWA5K08BgQUgwB5gw8hp8/1NyRN2fv+fbgQc0fBnn3BobiAjxaLBt4jCUOHHtmuLPn7HaDhGMGDIZnDjAYz8DnsPtvN0gcYDvMuOFG7jaJxAaglhkJDMY8+LQc4N3848C/w/Yb7r95RrSWbRIH2w4nbrjBwwbWIi9BUAv/N4uzfYeTd/akmUkkHDPmMeA52IDfLwfYkm9UfDtsu5398DPJHzVycvLtzceM8YUYBuAxOMDYZkyKDgYG+QYG5sekaRkFo2AUjIJhDgA+G1A8wmGBZQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Information and Communications Technology, and Osaka University","correspondingAuthor":true,"prefix":"","firstName":"Hiroki","middleName":"","lastName":"Watanabe","suffix":""},{"id":480665575,"identity":"76bd6038-4bb0-4c56-a181-2cdae56e18f1","order_by":1,"name":"Aya S. 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