EEG Channel Selection Based on Time–Frequency Hellinger Distance

EEG Channel Selection Based on Time–Frequency Hellinger Distance | 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Article EEG Channel Selection Based on Time–Frequency Hellinger Distance Seungjun Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6010191/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 This paper presents a novel method for EEG channel selection based on the Hellinger Distance (HD) computed over time–frequency representations (TFRs). Here, we first convert raw EEG into the time–frequency domain using Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). Leveraging Hellinger Distance, we then identify and remove outlier epochs via a z-score threshold, and rank and select the most discriminative channels by measuring how well each channel’s TFR distributions separate different classes. Our empirical evaluation uses the BCI Competition IVa dataset to compare the proposed HD-based approach (HD-CSP) against multiple variants of the Common Spatial Pattern algorithm, including standard CSP, L1 Norm CSP, SCSP, FBCSP, and E-CSP. Results indicate that HD-CSP consistently outperforms competing methods in all tested configurations, achieving notably high classification accuracy even when the number of channels is severely restricted. In particular, HD-CSP reaches around 70\% accuracy with only three channels, while other approaches suffer significant performance drops. As the number of channels increases, HD-CSP maintains its superior accuracy, exceeding 80% in some configurations. Overall, the proposed method is superior on performance gains and ability to adapt to diverse channel configurations suggest broad applicability, especially in resource-constrained EEG settings where efficiency and accuracy are both priorities. Biomedical Engineering EEG Electroencephalography Motor Imagery Hellinger Distance 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. 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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-6010191","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414416773,"identity":"6e16dfff-56d1-47d0-8003-6a5b57e8bf3b","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 20:18:39","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-6010191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6010191/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76172778,"identity":"d6dd7158-33d7-42f7-9c1b-901f4f6c95c1","added_by":"auto","created_at":"2025-02-13 05:42:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":691793,"visible":true,"origin":"","legend":"","description":"","filename":"HellingerDistance.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6010191/v1_covered_fdf5f870-e7e5-4c3d-a5ba-cdf1d062394b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEEG Channel Selection Based on Time–Frequency Hellinger Distance\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":"EEG, Electroencephalography, Motor Imagery, Hellinger Distance","lastPublishedDoi":"10.21203/rs.3.rs-6010191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6010191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a novel method for EEG channel selection based on the Hellinger Distance (HD) computed over time–frequency representations (TFRs). 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