EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction

EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction | 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 Physical Sciences - Article EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction Ruohan Zhang, Tasha Kim, Yingke Wang, Hanvit Cho, Alex Hodges, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7359180/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Brain–computer interfaces (BCIs) translate neural signals into control commands to restore or augment human capabilities, enabling robotic assistance for essential daily activities. Compared with intracortical BCIs, non-invasive BCIs require less medical and surgical intervention, but have yet to demonstrate reliable performance in complex everyday tasks with high success and low mental effort. We present Electroencephalography (EEG)-based Neural Signal Operated Intelligent Robots (NOIR-EEG), a general-purpose, intelligent, non-invasive BCI framework that allows users to command robots via EEG signals. NOIR-EEG combines advances in neural signal decoding with recent progress in AI and robotics, including large pre-trained models and intention learning. In tests, sixteen participants successfully completed fifteen challenging household tasks. Intention learning algorithms adapt to individual users and predict their goals, substantially reducing human effort. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Suppvideo1experimentwithoutlearningmode.mp4 Supplemental Video 1 Suppvideo2experimentwithlearningmode.mp4 Supplemental Video 2 Cite Share Download PDF Status: Under Review 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-7359180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":510162907,"identity":"0d5d1da1-443f-450e-8c3d-7fd7f65ad1c7","order_by":0,"name":"Ruohan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCRBRASIYHzAwsAHpA0RpOQOimA1I0MLYRooW+dnNzz7zzqur45/dzPi4ooxBju9GAn4tjHOOGc/m3XZYQuLOYWbDM+cYjCUJaWGWSDBm5t12QILhRv4xycY2hsQNhLSwSaR/ZuadUychfyOZDaSlnqAWHokcoC0NzBIGUC0JBoS0SEjkFAP9c1hy441kZsOGcxKGM888wK9Ffkb6ZoY3NXX8cjeSGR82lNnI8x0nYAuGraQpHwWjYBSMglGAHQAA4tc+6AFCF5UAAAAASUVORK5CYII=","orcid":"","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Ruohan","middleName":"","lastName":"Zhang","suffix":""},{"id":510162908,"identity":"86ad720d-6a93-4ad9-9cf4-8dcd07b57f87","order_by":1,"name":"Tasha Kim","email":"","orcid":"https://orcid.org/0009-0008-3701-0073","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Tasha","middleName":"","lastName":"Kim","suffix":""},{"id":510162909,"identity":"1f05a734-733b-4554-89b5-813cd840d556","order_by":2,"name":"Yingke Wang","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Yingke","middleName":"","lastName":"Wang","suffix":""},{"id":510162910,"identity":"42b18d32-7860-4ecd-aafe-8f59583d09fc","order_by":3,"name":"Hanvit Cho","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Hanvit","middleName":"","lastName":"Cho","suffix":""},{"id":510162911,"identity":"d11c7393-abfe-4aa1-a77c-d5455e854db3","order_by":4,"name":"Alex Hodges","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Hodges","suffix":""},{"id":510162912,"identity":"0c5019eb-73e0-4d0b-9673-d0f462d3804f","order_by":5,"name":"Jin Jie Ryan Tan","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Jie Ryan","lastName":"Tan","suffix":""},{"id":510162913,"identity":"ac95e493-ada7-4ce5-98c5-12ccebfc3726","order_by":6,"name":"Chen Wang","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":510162914,"identity":"74c3e22c-d210-4ac5-9ef3-8a22c6c4a5d8","order_by":7,"name":"Minjune Hwang","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Minjune","middleName":"","lastName":"Hwang","suffix":""},{"id":510162915,"identity":"bed0b26d-2fba-488d-8a31-95fbbbdbaebc","order_by":8,"name":"Sharon Lee","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Sharon","middleName":"","lastName":"Lee","suffix":""},{"id":510162916,"identity":"151b68dc-ec0a-40c2-9728-55267e1d75f2","order_by":9,"name":"Ayano Hiranaka","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Ayano","middleName":"","lastName":"Hiranaka","suffix":""},{"id":510162917,"identity":"504c8bfd-6b71-40df-8acf-e2053dc26aed","order_by":10,"name":"Wensi Ai","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Wensi","middleName":"","lastName":"Ai","suffix":""},{"id":510162918,"identity":"f1603a71-5536-4aad-a216-1a1f1b16b120","order_by":11,"name":"Anthony Norcia","email":"","orcid":"https://orcid.org/0000-0001-5471-3821","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Norcia","suffix":""},{"id":510162919,"identity":"a93c14b8-4ed6-4fe3-9e88-18b30f2a00aa","order_by":12,"name":"Li Fei-Fei","email":"","orcid":"https://orcid.org/0000-0002-7481-0810","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Fei-Fei","suffix":""},{"id":510162920,"identity":"03e9c30b-66bb-406a-8686-39029158be2b","order_by":13,"name":"Jiajun Wu","email":"","orcid":"https://orcid.org/0000-0002-4176-343X","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-08-12 21:25:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7359180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7359180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91692967,"identity":"148556fd-f745-4922-b0b2-26eb039158c2","added_by":"auto","created_at":"2025-09-19 09:04:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2287230,"visible":true,"origin":"","legend":"Article File","description":"","filename":"2024NatureNOIREEG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359180/v1_covered_77cdfb29-18a6-4956-8e87-592f806b60a5.pdf"},{"id":91692716,"identity":"6391e60d-21d8-46d0-a283-49bfdd30aff2","added_by":"auto","created_at":"2025-09-19 08:56:17","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27539621,"visible":true,"origin":"","legend":"Supplemental Video 1","description":"","filename":"Suppvideo1experimentwithoutlearningmode.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7359180/v1/54a5e40deb289b689681db03.mp4"},{"id":91692715,"identity":"9b313dd8-d164-4400-bc54-5ca7239fc19a","added_by":"auto","created_at":"2025-09-19 08:56:16","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25409464,"visible":true,"origin":"","legend":"Supplemental Video 2","description":"","filename":"Suppvideo2experimentwithlearningmode.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7359180/v1/e99153591474aa308604f73f.mp4"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7359180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7359180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brain–computer interfaces (BCIs) translate neural signals into control commands to restore or augment human capabilities, enabling robotic assistance for essential daily activities. Compared with intracortical BCIs, non-invasive BCIs require less medical and surgical intervention, but have yet to demonstrate reliable performance in complex everyday tasks with high success and low mental effort. We present Electroencephalography (EEG)-based Neural Signal Operated Intelligent Robots (NOIR-EEG), a general-purpose, intelligent, non-invasive BCI framework that allows users to command robots via EEG signals. NOIR-EEG combines advances in neural signal decoding with recent progress in AI and robotics, including large pre-trained models and intention learning. In tests, sixteen participants successfully completed fifteen challenging household tasks. Intention learning algorithms adapt to individual users and predict their goals, substantially reducing human effort.","manuscriptTitle":"EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 08:56:11","doi":"10.21203/rs.3.rs-7359180/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35de6322-d5a1-45ec-8144-15da44c1d292","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54187445,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":54187446,"name":"Physical sciences/Engineering/Biomedical engineering"}],"tags":[],"updatedAt":"2025-09-19T08:56:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 08:56:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7359180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7359180","identity":"rs-7359180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}