Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier

Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier | 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 Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier Navdeep Shakya, RAHUL DUBEY, Laxmi Shrivastava This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-782483/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Mental stress is currently a significant concern, especially among the young. Stress adversely affects the overall performance of people’s work, and in certain cases, can even cause serious health issues. Everyone experiences stress in life. A unique way to identify and classify stress levels based on Electroencephalogram (EEG) is proposed in this manuscript. In this work, fast Walsh Hadamard transform is used to generate all frequencies which exist in the EEG signals. The range of alpha, beta, gamma, and delta from index value is calculated in subsequent stage. Principal component analysis (PCA) is applied for the feature dimensional reduction which is followed by the standard scaler. The PSD vector has been calculated for healthy and unhealthy EEG signal groups using the Welch method. The PSD vector is used an input to the voting classifier which is the combination of the k-NN and logistic regression classifier. The experimental results found that the proposed method provides better results when compared to the existing methods in terms of Accuracy (Acc) and Mean Square Error (MSE). The proposed method achieves a highest classification accuracy of 94.22% Cell Communication and Signaling Theoretical Computer Science Brainwaves Mental Stress EEG Signal FWHT Voting Classifier. Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Reject after review 08 Aug, 2023 Reviewers agreed at journal 11 Jul, 2023 Reviewers invited by journal 18 Sep, 2021 Editor assigned by journal 05 Aug, 2021 First submitted to journal 04 Aug, 2021 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-782483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":52953145,"identity":"d9226bb1-c334-4032-9241-596c9c418e56","order_by":0,"name":"Navdeep Shakya","email":"","orcid":"","institution":"Madhav Institute of Technology and Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Navdeep","middleName":"","lastName":"Shakya","suffix":""},{"id":52953146,"identity":"0c750b88-d92e-4391-99d1-c5b1ba4165bd","order_by":1,"name":"RAHUL DUBEY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFACHgaGBxUgEgQKJIAEYwNhLQlnGHggegyI1ZLYxgC1xoAIZ5m3nz0mkThvm4w9//FrEj8MLOTMG5jbHuDTInMmL00icdttHh6JnDLJHgMJY5kDjO14LZNgyDGDauFJu8FjIJE4g4GxTQKvFv43QC1zgFr4z6Td/EOUFgmQLQ1ALQzpx24TZ4vEG2OLBKBinhs57L9lgH6RYCbosBzDGx9qbtuz9x9/bPimok5Ogr39GV4tSIAHGk7MRKoHAvYHxKsdBaNgFIyCEQUAC6o+4J9iEFMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9763-8005","institution":"Madhav Institute of Technology and Science","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"RAHUL","middleName":"","lastName":"DUBEY","suffix":""},{"id":52953147,"identity":"824a5375-be53-46b6-91b9-bcb584b3b866","order_by":2,"name":"Laxmi Shrivastava","email":"","orcid":"","institution":"Madhav Institute of Technology and Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laxmi","middleName":"","lastName":"Shrivastava","suffix":""}],"badges":[],"createdAt":"2021-08-04 22:46:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-782483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-782483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13822961,"identity":"827c1033-7599-4acf-9e82-638bf7162cb5","added_by":"auto","created_at":"2021-09-21 14:27:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":418924,"visible":true,"origin":"","legend":"","description":"","filename":"template.pdf","url":"https://assets-eu.researchsquare.com/files/rs-782483/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eStress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-782483/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":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wireless-personal-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wire","sideBox":"Learn more about [Wireless Personal Communications](https://www.springer.com/journal/11277)","snPcode":"11277","submissionUrl":"https://submission.nature.com/new-submission/11277/3","title":"Wireless Personal Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Brainwaves, Mental Stress, EEG Signal, FWHT, Voting Classifier.","lastPublishedDoi":"10.21203/rs.3.rs-782483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-782483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mental stress is currently a significant concern, especially among the young. 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