Zero-Shot Motor Imagery BCI via Self-Supervised Contrastive Learning

Zero-Shot Motor Imagery BCI via Self-Supervised Contrastive 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 Zero-Shot Motor Imagery BCI via Self-Supervised Contrastive Learning Branislav Ceperkovic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8011168/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 Self-supervised contrastive learning with SimCLR enables 62.1% zero-shot classification accuracy on unseen subjects, significantly outperforming the classical CSP+SVM pipeline (58.3%) in a 9- fold leave-one-subject-out evaluation on the MOABB BNCI2014001 dataset. Paired t-test confirms statistical significance: t(8) = 6.12, p < 0.001. This work demonstrates that self-supervised pretraining captures subject-invariant motor intention features, eliminating the need for per-user calibration and paving the way for practical, calibration-free BCIs. Computational Neuroscience brain-computer interface zero-shot learning self-supervised learning contrastive learning SimCLR EEG motor imagery cross-subject transfer Full Text Additional Declarations The authors declare no competing interests. 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-8011168","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538692850,"identity":"3b0c22f3-bc5c-4894-be4c-fad1eec89933","order_by":0,"name":"Branislav Ceperkovic","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-8482-3437","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Branislav","middleName":"","lastName":"Ceperkovic","suffix":""}],"badges":[],"createdAt":"2025-11-02 13:16:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8011168/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8011168/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95223977,"identity":"02be2314-c365-41ee-aedf-753a3a5e0a23","added_by":"auto","created_at":"2025-11-05 16:23:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":254131,"visible":true,"origin":"","legend":"","description":"","filename":"ZeroShotMotorImageryBCIviaSelfSupervisedContrastiveLearning1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8011168/v1_covered_68a596b6-73ab-419b-88e3-e3e87aed50f7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eZero-Shot Motor Imagery BCI via Self-Supervised Contrastive Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"brain-computer interface, zero-shot learning, self-supervised learning, contrastive learning, SimCLR, EEG, motor imagery, cross-subject transfer","lastPublishedDoi":"10.21203/rs.3.rs-8011168/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8011168/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelf-supervised contrastive learning with SimCLR enables 62.1% zero-shot classification accuracy on unseen subjects, significantly outperforming the classical CSP+SVM pipeline (58.3%) in a 9- fold leave-one-subject-out evaluation on the MOABB BNCI2014001 dataset. Paired t-test confirms statistical significance: t(8) = 6.12, p \u0026lt; 0.001. This work demonstrates that self-supervised pretraining captures subject-invariant motor intention features, eliminating the need for per-user calibration and paving the way for practical, calibration-free BCIs.\u003c/p\u003e","manuscriptTitle":"Zero-Shot Motor Imagery BCI via Self-Supervised Contrastive Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 05:42:47","doi":"10.21203/rs.3.rs-8011168/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"7a8756c3-b1f4-4c4f-bc2d-19ca9e5c6bc1","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57291867,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2025-11-04T05:42:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 05:42:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8011168","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8011168","identity":"rs-8011168","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}