Utilization of Mahalanobis Distance on Motor Imagery EEG Channel Selection

Utilization of Mahalanobis Distance on Motor Imagery EEG Channel Selection | 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 Utilization of Mahalanobis Distance on Motor Imagery EEG Channel Selection Seungjun Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6010188/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 In this study, a Mahalanobis distance based common spatial pattern (CSP)model (MD-CSP) was proposed and its performance applied to brain-computer interface (BCI) and motor imagery EEG signal processing was evaluated. Since EEG signals are often affected by various noises and artifacts, this study suggests a method of using Mahalanobis distance to remove noise and select important EEG channels. The experiment was conducted using the BCI competition IV 2a dataset and compared with CSPRank, L1 Norm of CSP, SCSPrank, FBCSPRank, and E-CSP. As a result of the experiment, MD-CSP performed particularly well at low number of channels and recorded an average accuracy of 60% in the two-channel configuration, outperforming 30%-43% accuracy in established models. MD-CSP showed better accuracy compared to other existing methods and showed a particularly noticeable difference in performance for less than five channels. This study shows that MD-CSP is effective in channel selection and noise removal in BCI system and suggests the possibility of application to real-time portable BCI system in the future. Biomedical Engineering Electroencephalography Motor Imagery Signal Processing 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-6010188","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414416593,"identity":"898bf462-c813-4d9b-a69a-baaba7bce387","order_by":0,"name":"Seungjun Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACfvYGhgMfKmwMgGw2IE4grEWy5wDjwxln0kjQYnAjgdmYt+UwCVoYDiSwSc5sOG8s336A7eEPhrR8gjoYGw6wSXzccdvM4EwCuzEPQ45lAyEtzIz93yRnnrkN9H8CmzQDQ4UBQVvYmBnYpHnbztnI9z9gk/xBjBYeNgag99sOmDHcSGCTADqMsBagKlAgJxsb3HjYJs1jkEZYi/39B6CotDOc3598TPJHRTJhLUiAsQEYTaRoGAWjYBSMglGAEwAAor84g/ryAA0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3630-9045","institution":"Korea Science Academy of KAIST","correspondingAuthor":true,"prefix":"","firstName":"Seungjun","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-02-11 20:15:39","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-6010188/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6010188/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76171636,"identity":"cc2a556b-628d-4fbe-abb6-9dfe23c7230c","added_by":"auto","created_at":"2025-02-13 05:35:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468112,"visible":true,"origin":"","legend":"","description":"","filename":"kdymjsdhcszpjjkvmccktpxjxkfjhzjm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6010188/v1_covered_41d9c2e5-6800-4e91-b907-ce12e086ce59.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eUtilization of Mahalanobis Distance on Motor Imagery EEG Channel Selection\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Korea Science Academy of KAIST","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Electroencephalography, Motor Imagery, Signal Processing","lastPublishedDoi":"10.21203/rs.3.rs-6010188/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6010188/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, a \u0026nbsp;Mahalanobis distance based common spatial pattern (CSP)model (MD-CSP) was proposed and its performance applied to brain-computer interface (BCI) and motor imagery EEG signal processing was evaluated. 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