FB-ACMCSP: A Filter-Bank Adaptive Multi-Class Common Spatial Pattern Framework for Cross-Subject Motor Imagery EEG Classification

FB-ACMCSP: A Filter-Bank Adaptive Multi-Class Common Spatial Pattern Framework for Cross-Subject Motor Imagery EEG Classification | 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-ACMCSP: A Filter-Bank Adaptive Multi-Class Common Spatial Pattern Framework for Cross-Subject Motor Imagery EEG Classification FOUAD CHOUAG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9296026/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 Inter-subject variability remains the principal challenge for zero-calibration brain-computer interface (BCI) deployment, where motor imagery (MI) EEG classifiers must generalize to completely unseen users without individual calibration. Existing Common Spatial Pattern (CSP) variants either ignore population-level statistics or fail to capture spectral diversity across the mu and beta frequency bands. This paper proposes FB-ACMCSP, a Filter-Bank Adaptive Multi-Class Common Spatial Pattern framework that combines: (1) filter-bank decomposition across nine overlapping sub-bands (8-30 Hz) to capture subject-specific spectral patterns; and (2) per-band adaptive covariance fusion that balances subject-specific and population-level covariance statistics via a fixed fusion parameter (alpha = 0.5). Preceded by Euclidean Alignment (EA) preprocessing, FB-ACMCSP is evaluated under a strict Leave-One-Subject-Out (LOSO) cross-subject protocol on the BCI Competition IV Dataset 2a (nine subjects, four MI classes). FB-ACMCSP achieves 41.28% mean LOSO accuracy and 69.64% within-subject 5-fold CV accuracy — outperforming CSP (38.46%), ACMCSP (38.93%), RCSP (38.89%), and Riemannian MDM (40.70%) — with consistent directional improvements in 6-7 of 9 subjects per comparison. The complete pipeline operates below 50 ms per trial on standard CPU hardware without GPU requirements, confirming real-time deployment suitability. All source code is publicly available at https://github.com/fouadchouag/FB-ACMCSP. Computational Neuroscience Computer Architecture and Engineering brain-computer interface motor imagery EEG common spatial pattern filter bank adaptive covariance fusion cross-subject classification Euclidean alignment LOSO evaluation zero-calibration BCI 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-9296026","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616180341,"identity":"d2cbe6df-3361-4dd6-9309-ab472a8759b3","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 21:21:47","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-9296026/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9296026/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106401992,"identity":"fa05b069-2764-4840-bb77-66cf88e7f538","added_by":"auto","created_at":"2026-04-08 09:10:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973192,"visible":true,"origin":"","legend":"","description":"","filename":"article3FBACMCSP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9296026/v1_covered_25819161-eb8e-481c-afbe-373784f1d6a1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFB-ACMCSP: A Filter-Bank Adaptive Multi-Class Common Spatial Pattern Framework for Cross-Subject Motor Imagery EEG Classification\u003c/strong\u003e\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, common spatial pattern, filter bank, adaptive covariance fusion, cross-subject classification, Euclidean alignment, LOSO evaluation, zero-calibration BCI","lastPublishedDoi":"10.21203/rs.3.rs-9296026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9296026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInter-subject variability remains the principal challenge for zero-calibration brain-computer interface (BCI) deployment, where motor imagery (MI) EEG classifiers must generalize to completely unseen users without individual calibration. Existing Common Spatial Pattern (CSP) variants either ignore population-level statistics or fail to capture spectral diversity across the mu and beta frequency bands. This paper proposes FB-ACMCSP, a Filter-Bank Adaptive Multi-Class Common Spatial Pattern framework that combines: (1) filter-bank decomposition across nine overlapping sub-bands (8-30 Hz) to capture subject-specific spectral patterns; and (2) per-band adaptive covariance fusion that balances subject-specific and population-level covariance statistics via a fixed fusion parameter (alpha = 0.5). Preceded by Euclidean Alignment (EA) preprocessing, FB-ACMCSP is evaluated under a strict Leave-One-Subject-Out (LOSO) cross-subject protocol on the BCI Competition IV Dataset 2a (nine subjects, four MI classes). FB-ACMCSP achieves 41.28% mean LOSO accuracy and 69.64% within-subject 5-fold CV accuracy — outperforming CSP (38.46%), ACMCSP (38.93%), RCSP (38.89%), and Riemannian MDM (40.70%) — with consistent directional improvements in 6-7 of 9 subjects per comparison. The complete pipeline operates below 50 ms per trial on standard CPU hardware without GPU requirements, confirming real-time deployment suitability. All source code is publicly available at https://github.com/fouadchouag/FB-ACMCSP.\u003c/p\u003e","manuscriptTitle":"FB-ACMCSP: A Filter-Bank Adaptive Multi-Class Common Spatial Pattern Framework for Cross-Subject Motor Imagery EEG Classification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 02:37:55","doi":"10.21203/rs.3.rs-9296026/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":"d62761dc-08b3-42ce-aa0d-fc9dc4930c02","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65570292,"name":"Computational Neuroscience"},{"id":65570293,"name":"Computer Architecture and Engineering"}],"tags":[],"updatedAt":"2026-04-03T02:37:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 02:37:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9296026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9296026","identity":"rs-9296026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}