FB-RCSP-RA: A Filter-Bank Regularized Common Spatial Pattern Framework with Per-Band Riemannian Alignment for Cross-Subject Motor Imagery EEG Decoding

FB-RCSP-RA: A Filter-Bank Regularized Common Spatial Pattern Framework with Per-Band Riemannian Alignment for Cross-Subject Motor Imagery EEG Decoding | 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 FB-RCSP-RA: A Filter-Bank Regularized Common Spatial Pattern Framework with Per-Band Riemannian Alignment for Cross-Subject Motor Imagery EEG Decoding FOUAD CHOUAG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9293689/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 Cross-subject motor imagery (MI) electroencephalography (EEG) decoding remains an open challenge in brain-computer interface (BCI) research, with inter-subject covariance shift being the principal obstacle to zero-calibration deployment. Existing Common Spatial Pattern (CSP) extensions address individual limitations — spectral heterogeneity, covariance ill-conditioning, or domain shift — but no unified framework has simultaneously integrated all three within a principled cross-subject pipeline. This paper proposes FB-RCSP-RA, a novel Filter-Bank Regularized CSP with Per-Band Riemannian Alignment that unifies: (1) filter-bank decomposition across nine overlapping sub-bands to capture the full mu and beta oscillatory spectrum; (2) per-band Ledoit-Wolf shrinkage regularization to ensure well-conditioned covariance estimates; and (3) per-band Riemannian alignment at the population Riemannian mean to normalize inter-subject covariance domain shift before spatial filter estimation. The framework is evaluated under a strict cross-subject Leave-One-Subject-Out (LOSO) protocol on BCI Competition IV Dataset 2a (nine subjects, four MI classes) with Euclidean Alignment preprocessing. FB-RCSP-RA achieves the highest mean LOSO accuracy of 42.98% and the highest within-subject 5-fold CV accuracy of 71.18% among all five evaluated methods, outperforming CSP (38.46%), ACMCSP (38.93%), RCSP (38.89%), and Riemannian MDM (40.70%). The consistent directional advantage across subjects (6/9 per comparison) and medium-to-large effect sizes (Cohen's d = 0.203 to 0.540) suggest genuine performance benefits. The pipeline operates below 40 ms per trial on standard CPU hardware without GPU requirements. All source code is publicly available at https://github.com/fouadchouag/FB-RCSP-RA. Computational Neuroscience Computer Architecture and Engineering brain-computer interface motor imagery EEG regularized common spatial pattern filter bank Riemannian alignment Ledoit-Wolf shrinkage cross-subject classification Euclidean alignment LOSO evaluation 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-9293689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616045001,"identity":"ed8f9c70-339c-48ec-8c4c-6d1cbf27f61b","order_by":0,"name":"FOUAD CHOUAG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFACxgdAgpmBQQLMswGJNB7Ar4XZAFlLGkhLA0laDoNJvFr4px1mfMxTY83AP7v5mMSPX+ft1rYfBtpSYxONS4vE7WRmY55j6QwSd46lSfb23U7ediYRqOVYWm4DLj23849J87AdZjCQyDGT4O25nWx2AKiFseEwTi3yt5PZpHn+QbRI/u05l2x2/iF+LQYgLbxtEC3SPD8O2JndIGCLIdAvhnP70nkkbqQlW8s2JCeY3QDakoDHL3K3kxkfvPlmLcc/I/ngzTd/7OzNzqc/fPChxga396GAB0wytjEkglUmEFCOBP4w2BOveBSMglEwCkYKAAAtbF+S4kWXGAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1261-845X","institution":"setif1 university Ferhat Abbas","correspondingAuthor":true,"prefix":"","firstName":"FOUAD","middleName":"","lastName":"CHOUAG","suffix":""}],"badges":[],"createdAt":"2026-04-01 15:10:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9293689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9293689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093974,"identity":"5e99935a-58e0-4341-9455-2fc52c8d11a7","added_by":"auto","created_at":"2026-04-03 11:40:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981347,"visible":true,"origin":"","legend":"","description":"","filename":"Art2FBRCSPRAIJIGSP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9293689/v1_covered_4b8fde12-20cc-4d52-abd8-215b8b8c9519.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFB-RCSP-RA: A Filter-Bank Regularized Common Spatial Pattern Framework with Per-Band Riemannian Alignment for Cross-Subject Motor Imagery EEG Decoding\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University Ferhat Abbas of Setif","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, motor imagery EEG, regularized common spatial pattern, filter bank, Riemannian alignment, Ledoit-Wolf shrinkage, cross-subject classification, Euclidean alignment, LOSO evaluation","lastPublishedDoi":"10.21203/rs.3.rs-9293689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9293689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCross-subject motor imagery (MI) electroencephalography (EEG) decoding remains an open challenge in brain-computer interface (BCI) research, with inter-subject covariance shift being the principal obstacle to zero-calibration deployment. 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