Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data

Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data | 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 Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data Perihan Gülşah GÜLHAN, Güzin ÖZMEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553208/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 Background: Simultaneous electroencephalography(EEG) and functional magnetic resonance imaging (fMRI) enable comprehensive investigationsof brain dynamics by combining high temporal and spatial resolutions. However, ballistocardiogram(BCG) artifacts in EEGsseverely affect signal quality and interpretation. This study aims to comparatively evaluate three widely used artifact removal techniques—average artifact subtraction (AAS), optimal basis set (OBS), and independent component analysis(ICA)—together with two hybrid approaches (AAS+ICA and OBS+ICA) within a multimodal and frequency-sensitive evaluation framework. Methods: Simultaneous EEG-fMRI data were preprocessed viafive artifact removal pipelines. Signal quality was assessed via time- and frequency-domain metrics, including power spectral density and correlation measures. Static and dynamic EEG-fMRI connectivity graphs were constructed on the basis of independent component networks (ICNs) and frequency-specific EEG features. Graph-theoretical measures (e.g., the clustering coefficient and global efficiency) were computed, and differences between methods were statistically evaluated via paired t tests with false discovery rate (FDR) correction. Results: AAS achieved the highest overall signal quality, whereasOBS preserved structural similarity more effectively. Although ICA yielded lower performance on traditional signal metrics, it demonstrated higher sensitivity to frequency-specific patterns, particularly in dynamic connectivity graphs. Among hybrid approaches, OBS+ICA provided the lowest p values across several frequency band pairs (e.g., theta–beta and delta–gamma), indicating improved detection of frequency-dependent interactions. Topological analyses revealed that artifact removal techniques substantially influence brain network organization. Dynamic connectivity analyses revealedstronger frequency-specific effects than static analyses did, with the beta and gamma bands showing the most pronounced differences. Conclusions: This study highlights the critical role of artifact removal strategies in shaping both EEG signal quality and EEG-fMRI connectivity outcomes. High-frequency bands, especially beta and gamma bands, exhibit distinctive network reconfigurations under dynamic conditions, underscoring their importance in cognitive and perceptual processes. By integrating signal-level, graph-theoretical, and multimodal evaluations, our findings provide practical guidelines for selecting preprocessing pipelines in simultaneous EEG-fMRI research and deepen our understanding of how methodological choices affect interpretations of brain connectivity. BCG artifact removal Brain graph metrics Multimodal data analysis Simultaneous EEG-fMRI Signal quality assessment Functional connectivity Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx 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-7553208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512838730,"identity":"35ede06c-c06d-4f96-ba56-0c52909d1526","order_by":0,"name":"Perihan Gülşah GÜLHAN","email":"data:image/png;base64,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","orcid":"","institution":"Selcuk University","correspondingAuthor":true,"prefix":"","firstName":"Perihan","middleName":"Gülşah","lastName":"GÜLHAN","suffix":""},{"id":512838736,"identity":"16325c82-96e7-4d14-bca0-417e671371ca","order_by":1,"name":"Güzin ÖZMEN","email":"","orcid":"","institution":"Selcuk University","correspondingAuthor":false,"prefix":"","firstName":"Güzin","middleName":"","lastName":"ÖZMEN","suffix":""}],"badges":[],"createdAt":"2025-09-06 22:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7553208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7553208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92625408,"identity":"82a7ee25-8d1e-4285-b3fb-a6ad8daca03c","added_by":"auto","created_at":"2025-10-01 22:46:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1246925,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7553208/v1_covered_d5b8deab-dd86-4418-a87e-702efd95f80b.pdf"},{"id":91037319,"identity":"43a6f731-bf98-4f8d-91a6-ef5d2a5d3b63","added_by":"auto","created_at":"2025-09-11 02:48:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35647,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7553208/v1/cac25128e63e4fbfce784b45.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data","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":"BCG artifact removal, Brain graph metrics, Multimodal data analysis, Simultaneous EEG-fMRI, Signal quality assessment, Functional connectivity","lastPublishedDoi":"10.21203/rs.3.rs-7553208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/em\u003eSimultaneous electroencephalography(EEG) and functional magnetic resonance imaging (fMRI) enable comprehensive investigationsof brain dynamics by combining high temporal and spatial resolutions. However, ballistocardiogram(BCG) artifacts in EEGsseverely affect signal quality and interpretation. This study aims to comparatively evaluate three widely used artifact removal techniques—average artifact subtraction (AAS), optimal basis set (OBS), and independent component analysis(ICA)—together with two hybrid approaches (AAS+ICA and OBS+ICA) within a multimodal and frequency-sensitive evaluation framework.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u003c/em\u003eSimultaneous EEG-fMRI data were preprocessed viafive artifact removal pipelines. Signal quality was assessed via time- and frequency-domain metrics, including power spectral density and correlation measures. Static and dynamic EEG-fMRI connectivity graphs were constructed on the basis of independent component networks (ICNs) and frequency-specific EEG features. Graph-theoretical measures (e.g., the clustering coefficient and global efficiency) were computed, and differences between methods were statistically evaluated via paired t tests with false discovery rate (FDR) correction.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u003c/em\u003eAAS achieved the highest overall signal quality, whereasOBS preserved structural similarity more effectively. Although ICA yielded lower performance on traditional signal metrics, it demonstrated higher sensitivity to frequency-specific patterns, particularly in dynamic connectivity graphs. Among hybrid approaches, OBS+ICA provided the lowest p values across several frequency band pairs (e.g., theta–beta and delta–gamma), indicating improved detection of frequency-dependent interactions. Topological analyses revealed that artifact removal techniques substantially influence brain network organization. Dynamic connectivity analyses revealedstronger frequency-specific effects than static analyses did, with the beta and gamma bands showing the most pronounced differences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003e\u003c/em\u003eThis study highlights the critical role of artifact removal strategies in shaping both EEG signal quality and EEG-fMRI connectivity outcomes. High-frequency bands, especially beta and gamma bands, exhibit distinctive network reconfigurations under dynamic conditions, underscoring their importance in cognitive and perceptual processes. By integrating signal-level, graph-theoretical, and multimodal evaluations, our findings provide practical guidelines for selecting preprocessing pipelines in simultaneous EEG-fMRI research and deepen our understanding of how methodological choices affect interpretations of brain connectivity.\u003c/p\u003e","manuscriptTitle":"Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 02:48:41","doi":"10.21203/rs.3.rs-7553208/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":"601aee01-e1f6-4141-8823-bd7e96d67688","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T22:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 02:48:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7553208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553208","identity":"rs-7553208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}