MCEEGNet: A Multi-Cue EEG Network for Quantitative Assessment of Depression Using Emotional Stimuli-Induced EEG Signals

MCEEGNet: A Multi-Cue EEG Network for Quantitative Assessment of Depression Using Emotional Stimuli-Induced 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 MCEEGNet: A Multi-Cue EEG Network for Quantitative Assessment of Depression Using Emotional Stimuli-Induced EEG Signals Xinmin Ding, Minpeng Xu, Jinghua Zhang, Jie Zhu, Xuenan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7405875/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 Depression significantly affects health, manifesting as alterations in typical emotional responses. Its diagnosis depends on subjective evaluations by clinicians, which are often time-intensive. Electroencephalogram (EEG) signals offer a viable solution for aiding diagnosis through computational means. However, current methods primarily focus on binary classification of depression, neglecting the quantification of depression risk. We propose the Multi-Cue EEG Network (MCEEGNet), which consists of parallel branches of EEGNet that extract features from various emotional stimuli to approximate scores on the Patient Health Questionnaire (PHQ-9). MCEEGNet aims to identify depression in patients using EEG signals and assess the severity of the condition. Our method achieved 91.13% accuracy in classification and reported Mean Squared Error (MSE) and Mean Absolute Error (MAE) of 20.45 and 3.28, respectively, in the Multi-Emotion Induced EEG Depression Database. The experimental outcomes suggest that MCEEGNet is highly effective in diagnosing subthreshold depression, offering a comprehensive system for evaluating clinical depression through EEG analysis influenced by multiple emotional cues, thereby meeting the need for quantitative depression evaluation. Depression Assessment Electroencephalogram (EEG) Multi-Cue EEG Network Emotional Stimuli Quantitative Evaluation Full Text Additional Declarations No competing interests reported. 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. 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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-7405875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518351494,"identity":"c448f1b7-5cd4-4acc-9874-2a3a668e7ca4","order_by":0,"name":"Xinmin Ding","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xinmin","middleName":"","lastName":"Ding","suffix":""},{"id":518351495,"identity":"9f1389b3-a824-4a4b-bb33-903cfbd08d86","order_by":1,"name":"Minpeng Xu","email":"","orcid":"","institution":"Tianjin 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Signals","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Depression Assessment, Electroencephalogram (EEG), Multi-Cue EEG Network, Emotional Stimuli, Quantitative Evaluation","lastPublishedDoi":"10.21203/rs.3.rs-7405875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7405875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDepression significantly affects health, manifesting as alterations in typical emotional responses. Its diagnosis depends on subjective evaluations by clinicians, which are often time-intensive. Electroencephalogram (EEG) signals offer a viable solution for aiding diagnosis through computational means. However, current methods primarily focus on binary classification of depression, neglecting the quantification of depression risk. We propose the Multi-Cue EEG Network (MCEEGNet), which consists of parallel branches of EEGNet that extract features from various emotional stimuli to approximate scores on the Patient Health Questionnaire (PHQ-9). MCEEGNet aims to identify depression in patients using EEG signals and assess the severity of the condition. Our method achieved 91.13% accuracy in classification and reported Mean Squared Error (MSE) and Mean Absolute Error (MAE) of 20.45 and 3.28, respectively, in the Multi-Emotion Induced EEG Depression Database. The experimental outcomes suggest that MCEEGNet is highly effective in diagnosing subthreshold depression, offering a comprehensive system for evaluating clinical depression through EEG analysis influenced by multiple emotional cues, thereby meeting the need for quantitative depression evaluation.\u003c/p\u003e","manuscriptTitle":"MCEEGNet: A Multi-Cue EEG Network for Quantitative Assessment of Depression Using Emotional Stimuli-Induced EEG Signals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 08:29:53","doi":"10.21203/rs.3.rs-7405875/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":"23c8f4a3-06a9-46c8-84d7-51221438d9d7","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T10:28:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 08:29:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7405875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7405875","identity":"rs-7405875","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}