A Distributed Adaptive Network Framework for Multi-Channel EEG Classification Using ERP Detection

A Distributed Adaptive Network Framework for Multi-Channel EEG Classification Using ERP Detection | 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 A Distributed Adaptive Network Framework for Multi-Channel EEG Classification Using ERP Detection fatemeh afkhaminia, Mohammad Bagher Shamsollahi, Tahereh Bahraini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5375537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. \color{green} Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes within the network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. \color{black} The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on synthetic, simulated, and real EEG datasets. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) \color{green} detection\color{black}, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications. Distributive Adaptive Networks Multichannel EEG signals Diffusion Strategy MultiTask Network Event-Related Potential (ERP) \color{green} detection \color{black} Adapt then Combine (ATC) algorithm Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor invited by journal 21 Apr, 2025 First submitted to journal 18 Apr, 2025 Editorial decision: Minor revisions 27 Dec, 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. 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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-5375537","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446739888,"identity":"188ae74f-8cb0-4257-ab8b-6bbcc130014b","order_by":0,"name":"fatemeh afkhaminia","email":"data:image/png;base64,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","orcid":"","institution":"Sharif University of Technology","correspondingAuthor":true,"prefix":"","firstName":"fatemeh","middleName":"","lastName":"afkhaminia","suffix":""},{"id":446739889,"identity":"8953b631-12d8-4042-a4ae-1f2606e3ab1e","order_by":1,"name":"Mohammad Bagher Shamsollahi","email":"","orcid":"","institution":"Sharif University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Bagher","lastName":"Shamsollahi","suffix":""},{"id":446739890,"identity":"3e65ccf4-6529-4821-a300-dd3ba5968903","order_by":2,"name":"Tahereh Bahraini","email":"","orcid":"","institution":"Sharif University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tahereh","middleName":"","lastName":"Bahraini","suffix":""}],"badges":[],"createdAt":"2024-11-01 21:11:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5375537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5375537/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13246-025-01578-2","type":"published","date":"2025-07-22T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87756660,"identity":"ca11a850-ae09-42ce-91ee-0c1052314ba5","added_by":"auto","created_at":"2025-07-28 16:06:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7754270,"visible":true,"origin":"","legend":"","description":"","filename":"APESD2400629R2reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5375537/v1_covered_17deaf93-cff6-4a88-b7e2-b515f8bafff2.pdf"}],"financialInterests":"","formattedTitle":"A Distributed Adaptive Network Framework for Multi-Channel EEG Classification Using ERP Detection","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Distributive Adaptive Networks, Multichannel EEG signals, Diffusion Strategy, MultiTask Network, Event-Related Potential (ERP) \\color{green} detection \\color{black}, Adapt then Combine (ATC) algorithm","lastPublishedDoi":"10.21203/rs.3.rs-5375537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5375537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding brain function is one of the most challenging areas in brain signal processing. 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