A Cloud-Edge Collaborative Framework for EEG-Based Depression Recognition via Universal Pretraining and Hierarchical Quality Control

A Cloud-Edge Collaborative Framework for EEG-Based Depression Recognition via Universal Pretraining and Hierarchical Quality Control | 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 Cloud-Edge Collaborative Framework for EEG-Based Depression Recognition via Universal Pretraining and Hierarchical Quality Control Yujie Zhang, Menglong Li, Weiqiang Zhang, Wei Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9123867/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Resting-state electroencephalography (EEG) is attractive for depression recognition, but practical deployment still faces scarce labels, unstable signal quality, and heterogeneous resource budgets across end, edge, and cloud layers. We therefore propose a cloud-edge collaborative framework that integrates self-supervised pretraining, downstream fine-tuning, and a hierarchical three-stage quality-control ladder with coarse screening (Q1), statistical refinement (Q2), and geometry-aware refinement (Q3). The framework is evaluated by pretraining on a large public healthy resting-state EEG dataset from OpenNeuro (ds005385), main validation on selected data from the public TDBRAIN and MODMA multi-channel resting-state datasets, and controlled robustness testing on EEGdenoiseNet.Three findings emerge. First, on TDBRAIN, pretrained initialization clearly outperforms random initialization, reaching 0.8734 versus 0.6825. Second, hierarchical quality control strengthens weaker baselines, improving scratch accuracy from 0.6825 under No-QC to 0.7339 with Q1+Q2 and 0.7659 with Q1+Q2+Q3.Third, resource profiling supports layered deployment: Q1 runs at 6.78 ms per epoch, the classifier head has 2242 parameters, and the encoder and pretraining model reach 49,504 and 90,419 parameters, respectively. These results provide a quantitative basis and practical design principle for cloud-edge collaborative EEG depression recognition. EEG depression recognition cloud-edge collaboration self-supervised learning quality-control ladder Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 14 Mar, 2026 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-9123867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609408269,"identity":"a291b947-41ab-46c6-a5b3-c3adf7d40caa","order_by":0,"name":"Yujie Zhang","email":"","orcid":"","institution":"Shenzhen MSU-BIT University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Zhang","suffix":""},{"id":609408272,"identity":"e3437259-16b2-4dd2-8e44-af45dc4ce12f","order_by":1,"name":"Menglong Li","email":"","orcid":"","institution":"Shenzhen MSU-BIT University","correspondingAuthor":false,"prefix":"","firstName":"Menglong","middleName":"","lastName":"Li","suffix":""},{"id":609408275,"identity":"866e29ee-fcce-4561-9bcd-5b72fcdee7d0","order_by":2,"name":"Weiqiang Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weiqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":609408278,"identity":"97a32256-4dba-488e-b7c4-2dc474c8cd8f","order_by":3,"name":"Wei Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZjApIcfAwIPgEqXFmAQtUJDYQLQWg+O8h1/zVFikz5929pgEQ4V1YgP72QN4tUg286VZ85yRyN1wOy9NguFMemIDT14CXi38zDxmxrltQC3SOWYSjG2HExskeAzwamEDa/knkS4/G6TlHxFagLYYP85tkEhguA3S0kCEFslmHjPmP8ckDIF+SbZIOJZu3MaTg1+Lwfkzxh9n1NTJy8/OPXjjQ421bD/7GfxaQN6RgDMTQFxC6oGA+QMRikbBKBgFo2AkAwCEKjuY4+BBOwAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen MSU-BIT University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2026-03-14 16:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9123867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9123867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563878,"identity":"49a9cd90-47c1-464b-8570-c2df846dee36","added_by":"auto","created_at":"2026-03-27 12:48:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2754602,"visible":true,"origin":"","legend":"","description":"","filename":"JoCC13677LaTeXSubmission20260314.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9123867/v1_covered_0d359b5b-7fd2-41e0-9d32-f124d7f23e47.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cloud-Edge Collaborative Framework for EEG-Based Depression Recognition via Universal Pretraining and Hierarchical Quality Control","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-cloud-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clco","sideBox":"Learn more about [Journal of Cloud Computing](http://journalofcloudcomputing.springeropen.com)","snPcode":"13677","submissionUrl":"https://submission.nature.com/new-submission/13677/3","title":"Journal of Cloud Computing","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EEG, depression recognition, cloud-edge collaboration, self-supervised learning, quality-control ladder","lastPublishedDoi":"10.21203/rs.3.rs-9123867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9123867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Resting-state electroencephalography (EEG) is attractive for depression recognition, but practical deployment still faces scarce labels, unstable signal quality, and heterogeneous resource budgets across end, edge, and cloud layers. 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