SSVEP-Based Machine Learning Solution to Classify Mild Traumatic Brain Injury

SSVEP-Based Machine Learning Solution to Classify Mild Traumatic Brain Injury | 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 SSVEP-Based Machine Learning Solution to Classify Mild Traumatic Brain Injury Quang Thien Hoang, Ken-Tye Yong, Dylan Mahony, Adrian Cohen, Barry Drake This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8905006/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 An objective diagnostic solution for mild traumatic brain injury (mTBI) has been a challenge in sports. The current standard diagnostic tools are considered unreliable due to their reliance on sign and symptom reporting which results in subjective outputs. There is an inherent risk that a mTBI may go unrecognised, which might have a long-term impact on an individual’s cognitive, emotional, and/or motor functions and therefore quality of life. We aimed to develop a solution which provides an objective mTBI assessment. A total of 516 Steady State Visual Evoked Potential (SSVEP) measurements were included in this study, collected from 425 non-mTBI participants and 91 mTBI participants assessed within 72 hours of the injury. Participants were categorised as mTBI following clinical diagnosis by an experienced physician. One minute of SSVEP data was collected from participants using three occipital electrodes using the Nurochek device. Multiple supervised machine learning algorithms were implemented to perform binary classification using various forms of transformed signals, including Fast Fourier Transformation, time-frequency decomposition, and demodulation. The reported results were from stratified k-fold cross-validation, where ‘k’ was the total number of mTBI in the training set. The best cross-validation performance was from Support Vector Machine with 86.19% sensitivity and 60.78% specificity. Using the same classifier on an independent testing set, a sensitivity of 82.00% and specificity of 64.89% were achieved. This research showed the benefits of utilising SSVEP measurements as potential biomarkers for diagnosing acute mTBI. The method and classifier developed could be implemented as a potential diagnostic tool. Full Text Additional Declarations Competing interest reported. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work contains a commercial conflict of interest by employees (Q.T.H, D.M) of Headsafe. A.J.C has filed a patent for the technology in this general area of mTBI diagnosis aids. B.D, K.Y declare no conflict of interest. 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-8905006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594953425,"identity":"812da7e4-78c0-479f-b0a3-d5e9160fb8f7","order_by":0,"name":"Quang Thien Hoang","email":"data:image/png;base64,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","orcid":"","institution":"The University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Quang","middleName":"Thien","lastName":"Hoang","suffix":""},{"id":594953429,"identity":"34275700-b846-4e3c-ab58-ecace5ffc3db","order_by":1,"name":"Ken-Tye Yong","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Ken-Tye","middleName":"","lastName":"Yong","suffix":""},{"id":594953431,"identity":"614ec6b4-e3fd-4347-9321-da0a8d9da7a5","order_by":2,"name":"Dylan Mahony","email":"","orcid":"","institution":"HeadsafeMFG","correspondingAuthor":false,"prefix":"","firstName":"Dylan","middleName":"","lastName":"Mahony","suffix":""},{"id":594953432,"identity":"6f23a92f-7230-46ea-b525-6c48673540ea","order_by":3,"name":"Adrian Cohen","email":"","orcid":"","institution":"HeadsafeMFG","correspondingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"","lastName":"Cohen","suffix":""},{"id":594953433,"identity":"bb06eb32-0059-4f20-9863-ff2fecc13a5e","order_by":4,"name":"Barry Drake","email":"","orcid":"","institution":"University of Technology Sydney","correspondingAuthor":false,"prefix":"","firstName":"Barry","middleName":"","lastName":"Drake","suffix":""}],"badges":[],"createdAt":"2026-02-18 01:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8905006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8905006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106487006,"identity":"8d6be2e9-d1d7-4a11-857d-7b829a6238f8","added_by":"auto","created_at":"2026-04-09 06:27:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1062544,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptnpj.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8905006/v1_covered_b09e6bf8-3ae2-45d1-910c-4af838282168.pdf"}],"financialInterests":"Competing interest reported. 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The current standard diagnostic tools are considered unreliable due to their reliance on sign and symptom reporting which results in subjective outputs. There is an inherent risk that a mTBI may go unrecognised, which might have a long-term impact on an individual\u0026rsquo;s cognitive, emotional, and/or motor functions and therefore quality of life. We aimed to develop a solution which provides an objective mTBI assessment. A total of 516 Steady State Visual Evoked Potential (SSVEP) measurements were included in this study, collected from 425 non-mTBI participants and 91 mTBI participants assessed within 72 hours of the injury. Participants were categorised as mTBI following clinical diagnosis by an experienced physician. One minute of SSVEP data was collected from participants using three occipital electrodes using the Nurochek device. 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