Electroencephalogram Electrode Selection and Parallel Merged Recurrent Neural Network for Emotion Classification

Electroencephalogram Electrode Selection and Parallel Merged Recurrent Neural Network for Emotion Classification | 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 Electroencephalogram Electrode Selection and Parallel Merged Recurrent Neural Network for Emotion Classification Ashish Sharma, Sarthak Singh, Sumit Srivastava, Manoj Bohra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625943/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 The field of biomedical signal analysis for the development of Brain Computer Interface(BCI) has seen significant improvement recently through the development of modern Deep Learning Algorithms. The Electroencephalogram(EEG) signal is non-stationary, non-linear, and contains a lot of noise as a result of aberrations brought on by muscle action, blinking and poor electrode contact, making research in this field particularly difficult.The number of electrodes used to record EEG signals on non-invasive wearable devices raises the dimensionality and, consequently, the computing complexity of the EEG data. We propose the reduction of said dimensionality through extensive feature extraction and channel selection. The proposed model also employs Merged RNN with the help of Long Short Term Memory (LSTMs) applied on features from each selected channel.The Dataset for Emotion Analysis using Physiological Signals (DEAP), the research standard for emotion recognition, has been used to validate the model's applicability and accuracy. 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-7625943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518848136,"identity":"0ea38a12-958b-42f3-b8b3-431125d0a727","order_by":0,"name":"Ashish Sharma","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Ashish","middleName":"","lastName":"Sharma","suffix":""},{"id":518848137,"identity":"74ec70a8-febb-47c3-8303-16bd7f4e7301","order_by":1,"name":"Sarthak Singh","email":"","orcid":"","institution":"Indian Institute of Information Technology Kota","correspondingAuthor":false,"prefix":"","firstName":"Sarthak","middleName":"","lastName":"Singh","suffix":""},{"id":518848138,"identity":"1309a3cc-68f7-49e8-83ef-05ce6c04624d","order_by":2,"name":"Sumit Srivastava","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Sumit","middleName":"","lastName":"Srivastava","suffix":""},{"id":518848139,"identity":"586fc2a1-140d-4d0b-8076-0dc36d0ddfdf","order_by":3,"name":"Manoj Bohra","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Bohra","suffix":""}],"badges":[],"createdAt":"2025-09-16 05:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7625943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7625943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92272916,"identity":"8bab4439-054d-4ab1-9a88-79358e49ceb7","added_by":"auto","created_at":"2025-09-26 15:01:08","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5349,"visible":true,"origin":"","legend":"","description":"","filename":"f6e9aa5681984bb9b70ba6fb1b214882.json","url":"https://assets-eu.researchsquare.com/files/rs-7625943/v1/41b4a268681fd285c8792fbc.json"},{"id":97899162,"identity":"84d52802-a218-43a2-ae99-c477379b6f2d","added_by":"auto","created_at":"2025-12-10 15:41:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":601930,"visible":true,"origin":"","legend":"","description":"","filename":"EEGemotionpaper2sumitsir1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7625943/v1_covered_cdea098b-c0f6-491e-af97-67f599b239a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electroencephalogram Electrode Selection and Parallel Merged Recurrent Neural Network for Emotion Classification","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7625943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7625943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The field of biomedical signal analysis for the development of Brain Computer Interface(BCI) has seen significant improvement recently through the development of modern Deep Learning Algorithms. 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