EEG-Based Biometric Authentication: Advancing Security Through Motor Imagery and Deep Learning

EEG-Based Biometric Authentication: Advancing Security Through Motor Imagery and Deep Learning | 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 EEG-Based Biometric Authentication: Advancing Security Through Motor Imagery and Deep Learning Mohammadreza Mostafavi, Ahmad Ayatollahi, Sivakumar Rajagopal, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6986340/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract EEG signals, reflecting synaptic activity across the brain's surface, offer a promising avenue for biometric authentication due to their resilience to spoofing attacks and immunity to coercion. This study leverages the BCI IV 2a dataset, where participants imagine movements of four body parts: right hand, left hand, both feet, and tongue. A two-dimensional CNN with 6 convolutional layers combined with the self-distillation technique was employed for classification. Various scenarios were analyzed based on task type, signal length, frequency band, and the number of EEG channels. Optimal results were achieved using a combination of frequency bands and the maximum number of channels, with a 4-second signal input yielding 100% accuracy and a 2-second input achieving 99.8397% accuracy for left-hand or tongue movement tasks. Despite these advancements, EEG-based authentication still faces challenges requiring further research to match the reliability and security of traditional biometric methods like fingerprint authentication. Authentication EEG Biometric Deep learning CNN Self-distillation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviewers invited by journal 07 Jul, 2025 Editor assigned by journal 27 Jun, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 26 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6986340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482103231,"identity":"b34e149d-fe24-4a12-a47f-cb58a3ac05fa","order_by":0,"name":"Mohammadreza Mostafavi","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Mostafavi","suffix":""},{"id":482103232,"identity":"0a2db0de-aa0b-4cf0-b57d-b44452cab3a7","order_by":1,"name":"Ahmad Ayatollahi","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Ayatollahi","suffix":""},{"id":482103233,"identity":"38e9f437-fcaa-4d95-aa9c-83806722e6d9","order_by":2,"name":"Sivakumar Rajagopal","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sivakumar","middleName":"","lastName":"Rajagopal","suffix":""},{"id":482103234,"identity":"fe242a6d-4900-4093-bb09-7497e22efb3c","order_by":3,"name":"Seok-Bum Ko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYHACZmYQKcHAfABEG5CihS2BZC08BsRpkW9vPmxc2LaNQbK955vkj5o6Ywb2ww/wajE4cyw5eWbbbQZpnrPbpHmOHTZj4EnDb5OBRI7xYV6gFjmJ3G3SDGwHbIBOxK9Ffv4bmJacZ5I//tUBtbB/wO+ZGzzGySAt0hI5bBK8bcxmDBI8BBx2Ji3ZeMa52zySPceMrXn7Dhuz8eQU4HdY++HD0gVlt+Ukjjc/vPnjW51hP/vxDfgdBgU8cBYbUepHwSgYBaNgFOAFAFOBPX7v+99LAAAAAElFTkSuQmCC","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":true,"prefix":"","firstName":"Seok-Bum","middleName":"","lastName":"Ko","suffix":""}],"badges":[],"createdAt":"2025-06-26 20:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6986340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6986340/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-025-04852-8","type":"published","date":"2025-10-03T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92883927,"identity":"11268d3c-32ab-4e6c-983a-ef577c57f2c0","added_by":"auto","created_at":"2025-10-06 16:11:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717276,"visible":true,"origin":"","legend":"","description":"","filename":"kvjtcykmrvntswjvxztjpjtzngzbytfr.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6986340/v1_covered_bd897523-91f4-4bd0-9459-da3050b81dba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EEG-Based Biometric Authentication: Advancing Security Through Motor Imagery and Deep Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Authentication, EEG, Biometric, Deep learning, CNN, Self-distillation","lastPublishedDoi":"10.21203/rs.3.rs-6986340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6986340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEEG signals, reflecting synaptic activity across the brain's surface, offer a promising avenue for biometric authentication due to their resilience to spoofing attacks and immunity to coercion. 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