Real-Time Hand Movement Detection using a Custom-Built EEG System

Real-Time Hand Movement Detection using a Custom-Built EEG System | 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 Article Real-Time Hand Movement Detection using a Custom-Built EEG System Nashwa Mosaad, Mohamad K. Refai, Khalid A. Elshafey, Basim M. Ayoub This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8264438/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 Spinal cord injury causes great disruption of voluntary hand movement, with consequent restrictions in functional independence. This work presents a real-time EEG-based detection system for hand opening and closing movements using a custom-built low-cost acquisition device. The proposed system includes eight EEG channels over motor-related cortical regions, amplified by AD620 instrumentation amplifiers, filtered through analog stages, and digitized with a 16-bit ADS1115 converter. EEG signals were recorded from healthy volunteers who continuously performed forced hand-opening and hand-closing tasks to capture clear cortical patterns associated with both states. Preprocessing included the reduction of artifacts, normalization, and feature selection based on signal variance to enhance the signal. Afterwards, a transformer-based deep learning model was developed to identify the hand state in real time with high accuracy and good temporal stability of the results. The results demonstrate that the combination of lightweight hardware with advanced neural network models allows for reliable detection of motor intent from non-invasive EEG signals. The results showed that the system achieved an accuracy of 97.78% with a latency less than 200 ms. This approach leads to low-cost neurotechnology for real-time assistive applications, pointing out the potentiality of its adaptation for restoring hand functionality in individuals with SCI. Biological sciences/Biological techniques Physical sciences/Engineering Biological sciences/Neuroscience Spinal cord injury (SCI) Brain-Computer Interface (BCI) Extreme Gradient Boosting (XGBoost) SMOTE: Synthetic Minority Over-sampling Technique Motor Imagery Electroencephalogram (EEG) and Analog to Digital Converter (ADC) Full Text Additional Declarations No competing interests reported. Supplementary Files supplementaryfile.docx 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-8264438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":563508129,"identity":"c98c4008-6515-4c1f-b60a-8b0806d9549c","order_by":0,"name":"Nashwa Mosaad","email":"data:image/png;base64,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","orcid":"","institution":"Al-Azhar University","correspondingAuthor":true,"prefix":"","firstName":"Nashwa","middleName":"","lastName":"Mosaad","suffix":""},{"id":563508130,"identity":"16fa1f5b-e746-4bbf-9472-9dc938693ab2","order_by":1,"name":"Mohamad K. 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System","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":"Spinal cord injury (SCI), Brain-Computer Interface (BCI), Extreme Gradient Boosting (XGBoost), SMOTE: 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This work presents a real-time EEG-based detection system for hand opening and closing movements using a custom-built low-cost acquisition device. The proposed system includes eight EEG channels over motor-related cortical regions, amplified by AD620 instrumentation amplifiers, filtered through analog stages, and digitized with a 16-bit ADS1115 converter. EEG signals were recorded from healthy volunteers who continuously performed forced hand-opening and hand-closing tasks to capture clear cortical patterns associated with both states. Preprocessing included the reduction of artifacts, normalization, and feature selection based on signal variance to enhance the signal. Afterwards, a transformer-based deep learning model was developed to identify the hand state in real time with high accuracy and good temporal stability of the results. The results demonstrate that the combination of lightweight hardware with advanced neural network models allows for reliable detection of motor intent from non-invasive EEG signals. The results showed that the system achieved an accuracy of 97.78% with a latency less than 200 ms. This approach leads to low-cost neurotechnology for real-time assistive applications, pointing out the potentiality of its adaptation for restoring hand functionality in individuals with SCI.\u003c/p\u003e","manuscriptTitle":"Real-Time Hand Movement Detection using a Custom-Built EEG System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 10:58:23","doi":"10.21203/rs.3.rs-8264438/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":"9d3ed0a7-705c-4517-8061-b8f8b01ff20d","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60007695,"name":"Biological sciences/Biological techniques"},{"id":60007696,"name":"Physical sciences/Engineering"},{"id":60007697,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-08T17:39:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 10:58:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8264438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8264438","identity":"rs-8264438","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}