Robust Brainprint Recognition via Session-wise Riemannian Alignment: Overcoming Non-stationary Brain Dynamics

Robust Brainprint Recognition via Session-wise Riemannian Alignment: Overcoming Non-stationary Brain Dynamics | 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 Robust Brainprint Recognition via Session-wise Riemannian Alignment: Overcoming Non-stationary Brain Dynamics Fanggang Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8802883/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 The non-stationarity of electroencephalography (EEG) signals constitutes a fundamental barrier to their application in reliable biometric identification. Due to fluctuations in cognitive states and environmental conditions, cross-session EEG signals exhibit significant distributional drift on the Riemannian manifold. This study proposes a robust brainprint recognition framework that integrates Session-wise Riemannian Centerization (S-RCT) with a global channel optimization strategy. By aligning the covariance matrices from different sessions to a common geometric reference, the proposed method effectively mitigates temporal variability. Experimental results on the BCI Competition IV 2a dataset demonstrate that this approach achieves a cross-session identification accuracy of 98.33% using 22 channels. Furthermore, topological analysis of channel configurations identifies an optimal 5-channel array centered on the parieto-occipital region. This minimal configuration maintains an accuracy of 90.89%, revealing the inherent dynamical stability of this brain region for identity representation. This research provides a geometric solution for constructing neuro-biometric systems robust to temporal drift. Brainprint recognition Non-stationary dynamics Riemannian manifold S-RCT Biometrics 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. 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-8802883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588442070,"identity":"a35235e3-397d-4413-81ea-250576aef3e9","order_by":0,"name":"Fanggang Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBAC9gbGB1Am84EPIIqNnYAWngPMBlAmW+IMMMVMvBYewxkQ2whpkUhm/Fzwq1benH/Nx4aPbdvk+ZgZGD98zMGrhVl6Zt9xw50z3m5snHHmtmEbMwOz5MxtuLXYS+QfkObtOca44cbZ7Y95Km4zArWwMfPi0QKy5TdQi/2GG2ceNvMY3LYnRgubNM+PmsQN53sYm4G2JBLWwvOYzZq34UDyhhtshiC/JLcxMzbj9QsPezLzbZ4/dbYbzh9+CAyx27bz25sPfviIRwsYMLYdZmCQSIBzGwioB4E/dQwM/AeIUDgKRsEoGAUjEgAAiNJUnywQU5cAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Fanggang","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2026-02-06 05:24:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8802883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8802883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104808698,"identity":"3c1c91cc-5bad-4b1b-b6d7-4802900b4e4c","added_by":"auto","created_at":"2026-03-17 12:39:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1228653,"visible":true,"origin":"","legend":"","description":"","filename":"CognitiveNeurodynamicsSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8802883/v1_covered_81496dca-1d66-4ebc-ad09-4a1577bf4551.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Brainprint Recognition via Session-wise Riemannian Alignment: Overcoming Non-stationary Brain Dynamics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brainprint recognition, Non-stationary dynamics, Riemannian manifold, S-RCT, Biometrics","lastPublishedDoi":"10.21203/rs.3.rs-8802883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8802883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe non-stationarity of electroencephalography (EEG) signals constitutes a fundamental barrier to their application in reliable biometric identification. 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