ETST: EEG Transformer for Person Identification

ETST: EEG Transformer for Person Identification | 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 ETST: EEG Transformer for Person Identification Yang Du, Yongling Xu, Xiaoan Wang, Li Liu, Pengcheng Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1545508/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract An increasing number of studies have been devoted to the use of electroencephalogram(EEG) for identity recognition due to the properties of EEG signals that are not easily stolen. Most of the existing studies on EEG person identification have only studied brain signals in a single state, requiring specific and repetitive sensory stimuli. However, the reality of human states is diverse and rapidly changing, which limits the use of their methods in realistic conditions. This demonstrates the excellent ability of the attention mechanism to model temporal signals. In this paper, we propose a transformer-based approach that extracts features in the temporal and spatial domains using a self-attention mechanism for the EEG person identification task. We conduct an extensive study to evaluate the generalization ability of the proposed method among different states. Our method is compared with the most advanced EEG biometrics techniques and the results show that our method reaches state-of-the-art results. Notably, we do not need to extract any features manually. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 20 May, 2022 Reviews received at journal 10 May, 2022 Reviewers agreed at journal 27 Apr, 2022 Reviewers invited by journal 24 Apr, 2022 Editor assigned by journal 13 Apr, 2022 Editor invited by journal 13 Apr, 2022 Submission checks completed at journal 13 Apr, 2022 First submitted to journal 11 Apr, 2022 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-1545508","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":98480597,"identity":"a16ee511-92b0-4c26-a41d-5633b0b5c717","order_by":0,"name":"Yang Du","email":"","orcid":"","institution":"Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Du","suffix":""},{"id":98480599,"identity":"7c475680-173a-4cba-9d79-f5709906d64a","order_by":1,"name":"Yongling Xu","email":"","orcid":"","institution":"Naolu Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongling","middleName":"","lastName":"Xu","suffix":""},{"id":98480602,"identity":"6d2c0e48-ae70-4209-84f7-a991691916b8","order_by":2,"name":"Xiaoan Wang","email":"","orcid":"","institution":"Naolu Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoan","middleName":"","lastName":"Wang","suffix":""},{"id":98480603,"identity":"c74c19c6-e2d7-46f2-ab4e-f6c78fb59b73","order_by":3,"name":"Li Liu","email":"","orcid":"","institution":"Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Liu","suffix":""},{"id":98480604,"identity":"21432ade-eb78-4520-bfca-309f341921aa","order_by":4,"name":"Pengcheng Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYNCCCjkwdeAB8VrOGEO0JBCtg7ENooWBKC3m7AdYN/ycZyBncO3wQ6AtdnK6DQS0WPYksN3s3WZgLDk7zQCoJdnY7AABLQY3GNhu8G77k9gvnQDSciBxGzFabv6dY5DYJp3+gXgtt3kbDIC25BBpi2VPYtttmWMgv+QUHEgwIMIv5uyHj918UwMMsdvpmz98qLCTI+x9BsYGFC5hQIyaUTAKRsEoGOkAAGjaQ6Q1H3YiAAAAAElFTkSuQmCC","orcid":"","institution":"Nanfang Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pengcheng","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2022-04-11 09:59:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1545508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1545508/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20471261,"identity":"edf08f05-1c49-448c-a7be-301d37dfae85","added_by":"auto","created_at":"2022-04-18 19:22:42","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":603772,"visible":true,"origin":"","legend":"","description":"","filename":"ETSTEEGTransformerforPersonIdentification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1545508/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ETST: EEG Transformer for Person Identification","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1545508/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1545508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1545508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"An increasing number of studies have been devoted to the use of electroencephalogram(EEG) for identity recognition due to the properties of EEG signals that are not easily stolen. 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