Determination of the time-frequency features for impulse components in EEG signals

Determination of the time-frequency features for impulse components in EEG signals | 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 Determination of the time-frequency features for impulse components in EEG signals Natalia Filimonova, Maria Specovius-Neugebauer, Elfriede Friedmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5269613/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Neuroinformatics → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose: Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. Inspired by the visual system's ability to identify objects and their locations, we propose a new method that integrates a visual system model with wavelet analysis to calculate both time and frequency features of local impulses in EEG signals. Methods: We develop a mathematical model based on invariant pattern recognition by the visual system, combined with wavelet analysis using Krawtchouk functions as the mother wavelet. Results: Our method precisely identifies the localization and frequency characteristics of the impulse components in EEG signals. Tested on task-related EEG data, it accurately detected blink components (0.5 to 1 Hz) and separated muscle artifacts (16 Hz). It also identified muscle response durations (298 ms) within the 1 to 31 Hz range in emotional reaction studies, offering insights into both individual and typical emotional responses. We further illustrated how the new method circumvents the uncertainty principle in low-frequency wavelet analysis. Conclusion: Unlike classical wavelet analysis, our method provides spectral characteristics of EEG impulses invariant to time shifts, improving the identification and classification of EEG components. visual system wavelet analysis Krawtchouk functions EEG processing Full Text Additional Declarations No competing interests reported. Supplementary Files SuplementaryFilimonovaFriedmann.zip Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Neuroinformatics → Version 1 posted Editorial decision: Revision requested 10 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 20 Oct, 2024 Reviewers invited by journal 20 Oct, 2024 Editor assigned by journal 16 Oct, 2024 Submission checks completed at journal 16 Oct, 2024 First submitted to journal 15 Oct, 2024 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. 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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-5269613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376304925,"identity":"00b9697e-fb0e-4898-8add-2e44321ae922","order_by":0,"name":"Natalia Filimonova","email":"","orcid":"","institution":"Taras Shevchenko National University of Kyiv","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Filimonova","suffix":""},{"id":376304926,"identity":"10ab862b-245f-4287-aab8-86a9c9252299","order_by":1,"name":"Maria Specovius-Neugebauer","email":"","orcid":"","institution":"University of Kassel","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Specovius-Neugebauer","suffix":""},{"id":376304927,"identity":"6b31bf0d-df2f-4b5e-ac00-cc661396df1d","order_by":2,"name":"Elfriede Friedmann","email":"data:image/png;base64,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","orcid":"","institution":"University of Kassel","correspondingAuthor":true,"prefix":"","firstName":"Elfriede","middleName":"","lastName":"Friedmann","suffix":""}],"badges":[],"createdAt":"2024-10-15 14:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5269613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5269613/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12021-024-09698-y","type":"published","date":"2025-01-23T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74859172,"identity":"b9091fb4-df2d-4c8a-8fcb-770024bfbcd5","added_by":"auto","created_at":"2025-01-27 16:13:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4361665,"visible":true,"origin":"","legend":"","description":"","filename":"FilimonovaFriedmannManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5269613/v1_covered_b4750c0c-0c19-4a6f-b767-e18564e1ac66.pdf"},{"id":70074655,"identity":"afde5442-eeb0-48b2-bee6-72acc8ec31b0","added_by":"auto","created_at":"2024-11-28 06:12:08","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":726993,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryFilimonovaFriedmann.zip","url":"https://assets-eu.researchsquare.com/files/rs-5269613/v1/7a4f136aaad1dc5fd7fa6779.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determination of the time-frequency features for impulse components in EEG signals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neuroinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nein","sideBox":"Learn more about [Neuroinformatics](http://link.springer.com/journal/12021)","snPcode":"12021","submissionUrl":"https://submission.nature.com/new-submission/12021/3","title":"Neuroinformatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"visual system, wavelet analysis, Krawtchouk functions, EEG processing","lastPublishedDoi":"10.21203/rs.3.rs-5269613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5269613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eAccurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. 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