EEG-Based Stress Classification Using Time-Domain Features and Segmentation Techniques

EEG-Based Stress Classification Using Time-Domain Features and Segmentation Techniques | 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 EEG-Based Stress Classification Using Time-Domain Features and Segmentation Techniques Usman Rauf, Anfal Zahid, Amna Qadeer, Adeel Zafar, Sheharyar Khan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491218/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Stress has been recognized as a significant global health issue and is estimated to affect the majority of the population. Rapid and accurate detection of stress is critical for stress treatment. A significant part of prior work has focused on classifying electroencephalography signals to enable preliminary detection of stress. Previous work has demonstrated considerable success in stress classification/detection using electroencephalography signals. The proposed work aims to classify human stress using electroencephalography signals to enable early intervention. We have worked with a dataset comprising nearly 215 individuals in this research and proposed a method based on time-domain analysis. Segmentation techniques are used to analyze stress from EEG signals. Both overlapping and non-overlapping methods are employed in this research work. The EEG signals last approximately 480 seconds. We have used the Perceived Stress Questionnaire (PSQ) for the labeled classes of ‘stressed’ and ‘non-stressed’. Different classifiers have been employed to distinguish between stressed and non-stressed classes. Our proposed method achieved an accuracy of 96.32% using a K-nearest neighbors classifier with a non-overlapping segmentation technique. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 27 Jan, 2026 Editor invited by journal 22 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 13 Jan, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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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-8491218","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580966961,"identity":"03680ecb-7986-458e-aef8-999abdabb11e","order_by":0,"name":"Usman 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