Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis

Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis | 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 Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis Michael Briden, Narges Norouzi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2121897/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jul, 2023 Read the published version in Biological Cybernetics → Version 1 posted 7 You are reading this latest preprint version Abstract We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion , is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions. Electroencephalogram Deep Learning Fusion Contrastive Representation Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jul, 2023 Read the published version in Biological Cybernetics → Version 1 posted Editorial decision: Major revision 22 Mar, 2023 Reviews received at journal 22 Dec, 2022 Reviewers agreed at journal 07 Nov, 2022 Reviewers invited by journal 18 Oct, 2022 Editor assigned by journal 12 Oct, 2022 Submission checks completed at journal 05 Oct, 2022 First submitted to journal 30 Sep, 2022 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-2121897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":142036015,"identity":"e676ea0b-21ef-4cc5-9c26-45e746484251","order_by":0,"name":"Michael Briden","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCQaGAzwMDHJQLjPxWoxJ08IA1JLYQLQW3dm9Dw+8qbiTvuH48YcfGCqsYXpxA7M7xw0OzjnzLHfDmRxjCYYz6URouZHGcJi37XDuhhs8bAyMbYeJ1fLvcLrBDfZnDIz/iNbScDjB4AaDGQNjAzFa7hxjODjn2GHDmSC/JBxLNyas5XYb84c3NYfl+UAh9qHGWpagFlSQQJryUTAKRsEoGAW4AACKWERxs+bnBAAAAABJRU5ErkJggg==","orcid":"","institution":"University of California, Santa Cruz","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Briden","suffix":""},{"id":142036017,"identity":"52d4dc4a-9e54-41e0-9016-56b603c57dc4","order_by":1,"name":"Narges Norouzi","email":"","orcid":"","institution":"University of California, Santa Cruz","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Narges","middleName":"","lastName":"Norouzi","suffix":""}],"badges":[],"createdAt":"2022-09-30 21:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2121897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2121897/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00422-023-00967-8","type":"published","date":"2023-07-04T21:32:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27617162,"identity":"3de9fa88-e9b1-44be-9be6-f788d381e0c3","added_by":"auto","created_at":"2022-10-11 14:32:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803496,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNeuroVision.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2121897/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-2121897/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bicy","sideBox":"Learn more about [Biological Cybernetics](http://link.springer.com/journal/422)","snPcode":"422","submissionUrl":"https://submission.nature.com/new-submission/422/3","title":"Biological Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Electroencephalogram, Deep Learning, Fusion, Contrastive Representation Learning","lastPublishedDoi":"10.21203/rs.3.rs-2121897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2121897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. 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