Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices

Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices | 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 Biological Sciences - Article Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices Dong Ming, Minpeng Xu, Xiaoyu Zhou, Xiaolin Xiao, Junyang Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7560506/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 Brain–computer interfaces (BCIs) represent a breakthrough that enables individuals to restore motor function and extend their ability to interact with external environments. Although invasive BCIs have demonstrated remarkable performance in neuroprosthetic control by directly decoding intracranial neuronal activities related to motor intention, their application is inevitably limited by surgical risks. Meanwhile, although noninvasive BCIs based on scalp electroencephalography (EEG) signals are theoretically safer for broader use, they are limited by inefficient and poor movement control performance, mainly owing to the lack of robust and efficient mapping between fine movement intention and EEG signals. To address this gap, we propose emulating the natural neuronal coding mechanisms of the brain to encode movement intention into EEG signals. We found that EEG potentials modulated by visual directions exhibit both single-channel cosine-like tuning and multichannel population coding patterns, resembling the motor directional tuning distribution of neuronal firing. Building on these findings, we encoded intended movement directions into EEG potentials modulated by visual directions and introduced a high-order multiscale discriminative analysis algorithm to decode the intended directions. We evaluated our approach through rigorous experiments involving cursor control, ground vehicle navigation, and quadcopter operation. Results from twenty-six participants demonstrated that our system could reach a median squared tracking correlation of 0.53, outperforming the best contemporary noninvasive BCI by 400% and even rivalling the performance of invasive counterparts. Notably, owing to its excellent performance, our approach could be applied in a high-cognitive-demand task, i.e., controlling a quadcopter pursuing, targeting, and photographing a moving vehicle. Thus, we established a neuronal direction-based coding framework to enhance EEG-based movement control, which represents the first application of neuronal coding principles to noninvasive BCIs. This approach narrows the performance gap with invasive systems, providing a safe and practical alternative to high-performance neuroprosthetics and enabling accurate and timely movement control in both daily-life interaction systems and unmanned operation scenarios. Biological sciences/Neuroscience/Motor control/Brain–machine interface Biological sciences/Neuroscience/Computational neuroscience/Neural encoding Biological sciences/Neuroscience/Computational neuroscience/Neural decoding Full Text Additional Declarations There is NO Competing Interest. Supplementary Files S1Neuralcursorfulltaskdemo.mp4 S1-Neural cursor-full task demo SIIncorporationofacodingschemebasedonneuronalfiringpatternsanddistributionsimprovesnoninvasivebraincontrolofroboticdevices.pdf SI_Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices S61Dverticalmovementtask.mp4 S6-1D vertical movement task S51Dhorizontalmovementtask.mp4 S5-1D horizontal movement task S2Unmannedvehicletypicaldemo.mp4 S2-Unmanned vehicle-typical demo S4CenterOuttask.mp4 S4-Center Out task S8Chasingandfleeingtask.mp4 S8-Chasing and fleeing task S7Tracking.mp4 S7-Tracking S10Quadcoptercontroltask.mp4 S10-Quadcopter control task S3Quadcoptercontroltypicaldemo.mp4 S3-Quadcopter control-typical demo S9Unmannedvehiclecontroltask.mp4 S9-Unmanned vehicle control task 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-7560506","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":530084489,"identity":"95679d1d-a3b1-4574-83f1-d4404eacbbcb","order_by":0,"name":"Dong Ming","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3RsQrCMBCA4StCXK52k4hDXyHFteCr1MXJwdHNQKUu2lkRfIa6OV4JONUncBF8gUIXkQ62S9dkFMy/hMB9hHAANtsPNgUUBMDXST8mKlcGJJCDZUNCJ8XbLD8UBkSQVzbH3DnzxUS5GyPiXtTyo3oMi5JcCb43JC3J1DFVjPX3GY2uEBxPkQFxdwoZ3jMKCojEQ0tQtIQzvnjSLDEl+J6LhgDlJiSQ7SsyjBjeRC4Lrv9Ls8pJhTWP/G38qupV6HtjDQH/CeAk3ZVrxrtq00GbzWb7y74jKE5dmQCroQAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Ming","suffix":""},{"id":530084490,"identity":"e8ca79be-8758-46e6-8102-4e214fb92e2b","order_by":1,"name":"Minpeng Xu","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Minpeng","middleName":"","lastName":"Xu","suffix":""},{"id":530084491,"identity":"398c18df-b289-48c0-91b0-75e1a84420d6","order_by":2,"name":"Xiaoyu Zhou","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Zhou","suffix":""},{"id":530084492,"identity":"88186efc-7932-4be0-bab6-b5f84fb634a7","order_by":3,"name":"Xiaolin Xiao","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Xiao","suffix":""},{"id":530084493,"identity":"8cf2619a-85f4-49f0-bd4c-047edfe7e2b0","order_by":4,"name":"Junyang Wang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Junyang","middleName":"","lastName":"Wang","suffix":""},{"id":530084494,"identity":"23447256-ddf9-478c-8322-cb085f6c8c4a","order_by":5,"name":"Yongzhi Huang","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Yongzhi","middleName":"","lastName":"Huang","suffix":""},{"id":530084495,"identity":"1cffc320-1b45-4092-b638-d20e691a1c03","order_by":6,"name":"Kun Wang","email":"","orcid":"https://orcid.org/0000-0002-9365-5107","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":530084496,"identity":"6c73e07b-1722-4f6b-a582-b87fa9c5c615","order_by":7,"name":"Shuang Liu","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Liu","suffix":""},{"id":530084497,"identity":"349bfc62-7ac9-4181-9dbc-19c2b2f130b2","order_by":8,"name":"Weibo Yi","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Weibo","middleName":"","lastName":"Yi","suffix":""},{"id":530084498,"identity":"21c0b047-1e92-42ed-8edd-1d97b2f718c1","order_by":9,"name":"Jing Jin","email":"","orcid":"","institution":"East China University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jin","suffix":""},{"id":530084499,"identity":"05bd3b10-bee5-421b-8fed-85eb5803491e","order_by":10,"name":"Xiaojian Li","email":"","orcid":"https://orcid.org/0000-0001-8130-0731","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaojian","middleName":"","lastName":"Li","suffix":""},{"id":530084500,"identity":"87969989-37ef-4615-9cf6-6c733e656477","order_by":11,"name":"Tzyy-Ping Jung","email":"","orcid":"","institution":"University of California; San Diego","correspondingAuthor":false,"prefix":"","firstName":"Tzyy-Ping","middleName":"","lastName":"Jung","suffix":""}],"badges":[],"createdAt":"2025-09-08 06:10:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7560506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7560506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96256015,"identity":"27c52c8d-829e-4bae-94e9-2093236059b2","added_by":"auto","created_at":"2025-11-19 07:49:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4282207,"visible":true,"origin":"","legend":"Article File","description":"","filename":"FullTextIncorporationofacodingschemebasedonneuronalfiringpatternsanddistributionsimprovesnoninvasivebraincontrolofroboticdevices.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1_covered_ee9588a3-b07a-42da-9ad6-00d63457dfce.pdf"},{"id":96058786,"identity":"14c4228a-ad10-48b9-b21b-7f040f804839","added_by":"auto","created_at":"2025-11-17 08:16:29","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3244600,"visible":true,"origin":"","legend":"S1-Neural cursor-full task demo","description":"","filename":"S1Neuralcursorfulltaskdemo.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/964e63a48812f195257863cf.mp4"},{"id":96247648,"identity":"18d88ab0-ad73-4adf-87b9-f9406e8e3794","added_by":"auto","created_at":"2025-11-19 07:27:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4787702,"visible":true,"origin":"","legend":"SI_Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices","description":"","filename":"SIIncorporationofacodingschemebasedonneuronalfiringpatternsanddistributionsimprovesnoninvasivebraincontrolofroboticdevices.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/e0c3cc8fed7530e32b9fc593.pdf"},{"id":96058789,"identity":"a3a5ae04-0035-4098-93a8-26a274a8f9c0","added_by":"auto","created_at":"2025-11-17 08:16:29","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7848856,"visible":true,"origin":"","legend":"S6-1D vertical movement task","description":"","filename":"S61Dverticalmovementtask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/ea6e24fc5bce2b719b30bfc3.mp4"},{"id":96058790,"identity":"20698341-eaeb-440b-b26d-7d59a96574f0","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":8046098,"visible":true,"origin":"","legend":"S5-1D horizontal movement task","description":"","filename":"S51Dhorizontalmovementtask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/e0ab782a9b9f04b5389635f8.mp4"},{"id":96058788,"identity":"ff42c44f-86d7-4134-812c-07b992418071","added_by":"auto","created_at":"2025-11-17 08:16:29","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9193786,"visible":true,"origin":"","legend":"S2-Unmanned vehicle-typical demo","description":"","filename":"S2Unmannedvehicletypicaldemo.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/cc21a32b33f0406fd567a186.mp4"},{"id":96058792,"identity":"b6d20032-8a1d-4e9b-a794-5c90f85cd4a7","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9081397,"visible":true,"origin":"","legend":"S4-Center Out task","description":"","filename":"S4CenterOuttask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/00f8b11713813f8da90d968b.mp4"},{"id":96058796,"identity":"14120c68-136a-49cb-9b8e-59a0c2d04fe2","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9555990,"visible":true,"origin":"","legend":"S8-Chasing and fleeing task","description":"","filename":"S8Chasingandfleeingtask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/c5accff541f5a6ea3c02f585.mp4"},{"id":96058791,"identity":"03c7cc2a-254d-4030-b72e-54fd6f362f32","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11249900,"visible":true,"origin":"","legend":"S7-Tracking","description":"","filename":"S7Tracking.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/9a8517891a49a1c9652d69ce.mp4"},{"id":96058793,"identity":"aaa9e9c7-1278-49e3-8f66-01bda32db720","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":19203991,"visible":true,"origin":"","legend":"S10-Quadcopter control task","description":"","filename":"S10Quadcoptercontroltask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/4c70d056fb7945e1de565edb.mp4"},{"id":96058794,"identity":"cc65c399-a573-44f0-b123-04246bbf9e4e","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":23034010,"visible":true,"origin":"","legend":"S3-Quadcopter control-typical demo","description":"","filename":"S3Quadcoptercontroltypicaldemo.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/e6807176ccbcb6e3ba1d5027.mp4"},{"id":96058795,"identity":"be808aa1-5bb7-40a8-b67d-59d4c3213af4","added_by":"auto","created_at":"2025-11-17 08:16:30","extension":"mp4","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":10122454,"visible":true,"origin":"","legend":"S9-Unmanned vehicle control task","description":"","filename":"S9Unmannedvehiclecontroltask.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7560506/v1/3f2faca66af054653f38b454.mp4"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7560506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7560506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brain–computer interfaces (BCIs) represent a breakthrough that enables individuals to restore motor function and extend their ability to interact with external environments. Although invasive BCIs have demonstrated remarkable performance in neuroprosthetic control by directly decoding intracranial neuronal activities related to motor intention, their application is inevitably limited by surgical risks. Meanwhile, although noninvasive BCIs based on scalp electroencephalography (EEG) signals are theoretically safer for broader use, they are limited by inefficient and poor movement control performance, mainly owing to the lack of robust and efficient mapping between fine movement intention and EEG signals. To address this gap, we propose emulating the natural neuronal coding mechanisms of the brain to encode movement intention into EEG signals. We found that EEG potentials modulated by visual directions exhibit both single-channel cosine-like tuning and multichannel population coding patterns, resembling the motor directional tuning distribution of neuronal firing. Building on these findings, we encoded intended movement directions into EEG potentials modulated by visual directions and introduced a high-order multiscale discriminative analysis algorithm to decode the intended directions. We evaluated our approach through rigorous experiments involving cursor control, ground vehicle navigation, and quadcopter operation. Results from twenty-six participants demonstrated that our system could reach a median squared tracking correlation of 0.53, outperforming the best contemporary noninvasive BCI by 400% and even rivalling the performance of invasive counterparts. Notably, owing to its excellent performance, our approach could be applied in a high-cognitive-demand task, i.e., controlling a quadcopter pursuing, targeting, and photographing a moving vehicle. Thus, we established a neuronal direction-based coding framework to enhance EEG-based movement control, which represents the first application of neuronal coding principles to noninvasive BCIs. This approach narrows the performance gap with invasive systems, providing a safe and practical alternative to high-performance neuroprosthetics and enabling accurate and timely movement control in both daily-life interaction systems and unmanned operation scenarios.","manuscriptTitle":"Incorporation of a coding scheme based on neuronal firing patterns and distributions improves noninvasive brain control of robotic devices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 08:16:24","doi":"10.21203/rs.3.rs-7560506/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-human-behaviour","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathumbehav","sideBox":"Learn more about [Nature Human Behaviour](http://www.nature.com/nathumbehav/)","snPcode":"","submissionUrl":"","title":"Nature Human Behaviour","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e09b54a1-99fc-4299-adf5-e3149808f800","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56349008,"name":"Biological sciences/Neuroscience/Motor control/Brain\u0026#x2013;machine interface"},{"id":56349009,"name":"Biological sciences/Neuroscience/Computational neuroscience/Neural encoding"},{"id":56349010,"name":"Biological sciences/Neuroscience/Computational neuroscience/Neural decoding"}],"tags":[],"updatedAt":"2026-04-30T14:27:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 08:16:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7560506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7560506","identity":"rs-7560506","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}