Neuro-AI for Sports: Brainwave-Informed Performance Prediction Using Deep Learning and EEG Analysis

Neuro-AI for Sports: Brainwave-Informed Performance Prediction Using Deep Learning and 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 Neuro-AI for Sports: Brainwave-Informed Performance Prediction Using Deep Learning and EEG Analysis Neha, Aditya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9479324/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 Neuroscience and artificial intelligence, the convergence of neuroscience and artificial intelligence has presented revolutionary sports performance optimization. This paper is a comprehensive Neuro. The AI system based on electroencephalography (EEG) signals and athletic predictive and enhancement deep learning architectures. performance with analytics of the brain. We developed a multi-level pipeline with the EEG signal acquisition, preprocessing Independent Component Analysis (ICA), neural biomarkers feature extraction (Sensorimotor) Classification using; rhythm, alpha, theta bands) and classification. Convolutional neural networks, Gated Recurrent Units (GRU). Transformer architecture, (CNN), and Transformer architecture. Our system achieved great precision with the Transformer model (98.1%), GRU model (97.8%), CNN model (94.3%), and 3–13% proves to be better than 70.5%-86.5% by 1.2%-1.8%. better than available state-of-the-art methods. The system distinguishes well between focused, fatigued, stressed and relaxed mental conditions having a time of inference less than 200 milliseconds, to render it appropriate to real-time applications. Cross-subject vali dation was accurate in 92.7% in 45 athletes of 5 sport. disciplines. Performance was illustrated by real-life case studies. increases of 6–12% in high performance athletes. This work establishes an effective architecture of sports science in the next generation, which allows. objective detection of mental fatigue, individual neurofeedback. performance forecasting, performance, and training. Artificial Intelligence and Machine Learning Cellular & Molecular Neuroscience Sports Medicine and Kinesiology Biomedical Engineering Deep Learning Electroencephalography Sports. Receptor Neural Biomarkers Gated Recurrent Units Trans. formers brain-computer interface Neurofeedback Full Text Additional Declarations The authors declare no competing interests. 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-9479324","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626763887,"identity":"df39d8e8-952b-4fb9-9bc7-1bd459da1943","order_by":0,"name":"Neha","email":"data:image/png;base64,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","orcid":"","institution":"Galgotias University","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Neha","suffix":""},{"id":626763888,"identity":"ed8f6521-c502-4bfb-bebb-f2a537d4af99","order_by":1,"name":"Aditya","email":"","orcid":"","institution":"Galgotias University","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Aditya","suffix":""}],"badges":[],"createdAt":"2026-04-21 05:55:12","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9479324/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9479324/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705536,"identity":"bb0f2f9c-2d98-4e54-b0aa-5a8f1064885e","added_by":"auto","created_at":"2026-04-24 09:13:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":269085,"visible":true,"origin":"","legend":"","description":"","filename":"NeuroAIforSportsIEEEPaper21.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9479324/v1_covered_b0286f0f-723d-4c93-b9a1-d68407b4f932.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNeuro-AI for Sports: Brainwave-Informed Performance Prediction Using Deep Learning and EEG Analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Deep Learning, Electroencephalography, Sports. 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This work establishes an effective architecture of sports science in the next generation, which allows. objective detection of mental fatigue, individual neurofeedback. performance forecasting, performance, and training.\u003c/p\u003e","manuscriptTitle":"Neuro-AI for Sports: Brainwave-Informed Performance Prediction Using Deep Learning and EEG Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 04:05:52","doi":"10.21203/rs.3.rs-9479324/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":"81c3a512-350e-4ddc-ae5f-fe687824380f","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66701926,"name":"Artificial Intelligence and Machine Learning"},{"id":66701927,"name":"Cellular \u0026 Molecular Neuroscience"},{"id":66701928,"name":"Sports Medicine and Kinesiology"},{"id":66701929,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2026-04-22T04:05:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 04:05:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9479324","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9479324","identity":"rs-9479324","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}