Dynamically Optimized SVDD Based Mental State Recognition Method

Dynamically Optimized SVDD Based Mental State Recognition Method | 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 Dynamically Optimized SVDD Based Mental State Recognition Method Yongheng Pang, Yongling Liang, Nan Jiang, Mengxiang Wang, Jia Qin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8640672/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 Intelligent mental state detection methods require training based on a large amount of foundational data. However, in real-world applications, it is often challenging to obtain sufficient data, which limits the applicability of these methods. Therefore, this paper proposes a mental state prediction and recognition method with dynamically updated classification boundaries, capable of achieving precise and timely diagnosis based on a small number of real-time target samples. The paper introduces a dual-boundary SVDD method based on a relaxation threshold. By applying relaxation variables, this method can accurately filter out data points located between the core normal region and the potential abnormal region, thereby determining the range of effective support vectors. The paper also proposes a model dynamic updating method based on spatial/temporal weighting, which improves the training efficiency and shortens the modeling cycle. Additionally, a classification hypersphere's center trajectory offset index is proposed, which uses offset acceleration to identify mental abnormal states, thereby enhancing accuracy and timeliness. The proposed method does not require a large amount of target sample data in advance and can adjust the induction or pre-screening strategy in real-time according to the situation. The model undergoes online training and updating throughout the entire process, making it suitable for precise mental recognition in real-world scenarios. To verify the effectiveness of the proposed method, the SEED dataset from the BCMI laboratory at Shanghai Jiao Tong University and the publicly available DEAP emotional dataset were used for validation. The experimental results confirm the effectiveness and superiority of this method in practical applications. Mental State Recognition Dynamic Classification Boundaries Support Vector Description Spatial Weight Hypersphere Center Offset 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. 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-8640672","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589794005,"identity":"39102a7c-0d9c-4c75-8153-f75671e63199","order_by":0,"name":"Yongheng Pang","email":"","orcid":"","institution":"Criminal Investigation Police University of China","correspondingAuthor":false,"prefix":"","firstName":"Yongheng","middleName":"","lastName":"Pang","suffix":""},{"id":589794006,"identity":"033b1cb3-1402-4ada-a7f5-ee773cb8d22d","order_by":1,"name":"Yongling Liang","email":"","orcid":"","institution":"Criminal Investigation Police University of China","correspondingAuthor":false,"prefix":"","firstName":"Yongling","middleName":"","lastName":"Liang","suffix":""},{"id":589794007,"identity":"a88c373d-b0ba-454c-8ac6-92b31fcde0d0","order_by":2,"name":"Nan Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCQjJzMbAfADIsGBg4CFeC1sCA0OCBNFaQIDHgDgt8rN7DB/zVFiw80n3fHzM+0NCjp/nAOOHjzm4tRjcOWNszHMG6DCZs5uNeRIkjCV7G5glZ27Do0Uix0w6tw2oRSJ3mzRQS+KG8wxszLx4tMjPAGn5B9KS84w4LQw3QFoawFrYIFrONuDXYnAjrdj4zzGQljRjwzlpQL/0HGzG6xf5GckbH86oqUsGMh4+eGNjAwyx5IMfPuJzGBQkI7EZGwirBwI7olSNglEwCkbByAQA9T5CS9kWCY4AAAAASUVORK5CYII=","orcid":"","institution":"Criminal Investigation Police University of China","correspondingAuthor":true,"prefix":"","firstName":"Nan","middleName":"","lastName":"Jiang","suffix":""},{"id":589794008,"identity":"71439286-1990-4e50-af93-6b4da00e373e","order_by":3,"name":"Mengxiang Wang","email":"","orcid":"","institution":"China National Institute of Standardization","correspondingAuthor":false,"prefix":"","firstName":"Mengxiang","middleName":"","lastName":"Wang","suffix":""},{"id":589794010,"identity":"2f7ba10a-6e36-4852-b071-2de56fa9b677","order_by":4,"name":"Jia Qin","email":"","orcid":"","institution":"Criminal Investigation Police University of China","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Qin","suffix":""},{"id":589794012,"identity":"27c536f2-01f1-447a-b549-beaa598305ad","order_by":5,"name":"Shuowei Jin","email":"","orcid":"","institution":"Northeast University","correspondingAuthor":false,"prefix":"","firstName":"Shuowei","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2026-01-19 15:07:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8640672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8640672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108006763,"identity":"c932e7ed-747e-4c59-9e76-2afa29e96e31","added_by":"auto","created_at":"2026-04-28 12:56:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901472,"visible":true,"origin":"","legend":"","description":"","filename":"Springer3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8640672/v1_covered_70484512-ca8a-4a2e-8b56-c4952b7d8d9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamically Optimized SVDD Based Mental State Recognition Method","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":"Mental State Recognition, Dynamic Classification Boundaries, Support Vector Description, Spatial Weight, Hypersphere Center Offset","lastPublishedDoi":"10.21203/rs.3.rs-8640672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8640672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Intelligent mental state detection methods require training based on a large amount of foundational data. However, in real-world applications, it is often challenging to obtain sufficient data, which limits the applicability of these methods. Therefore, this paper proposes a mental state prediction and recognition method with dynamically updated classification boundaries, capable of achieving precise and timely diagnosis based on a small number of real-time target samples. The paper introduces a dual-boundary SVDD method based on a relaxation threshold. By applying relaxation variables, this method can accurately filter out data points located between the core normal region and the potential abnormal region, thereby determining the range of effective support vectors. The paper also proposes a model dynamic updating method based on spatial/temporal weighting, which improves the training efficiency and shortens the modeling cycle. 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