Transcending ECoG Spatial Sampling Limits: Expanding Virtual Neural Landscapes with Generative AI for Motor Decoding

Transcending ECoG Spatial Sampling Limits: Expanding Virtual Neural Landscapes with Generative AI for Motor Decoding | 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 Transcending ECoG Spatial Sampling Limits: Expanding Virtual Neural Landscapes with Generative AI for Motor Decoding June Sic Kim, Seyoung Shin, Dahye Kim, Chun Kee Chung, Sung-Phil Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8872713/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 Electrocorticography (ECoG)-based brain-computer interfaces (BCIs) offer high-fidelity motor control, yet their utility is fundamentally constrained by surgical window. Clinical risks often restrict electrode placement, imposing a “spatial sampling limit” that impairs decoding performance. In this study, we present a generative AI framework based on Denoising Diffusion Probabilistic Models (DDPM) that transcends these limitations by synthesizing physiologically plausible virtual ECoG signals in unrecorded cortical regions. By conditioning the generative process on recorded signals, our framework reconstructs missing neural landscapes. Intra-subject validation confirms that the proposed framework harnesses cross-regional dependencies manifested in movements to generate virtual signals mirroring real neural counterparts. We then take an inter-subject transfer approach, using population-level inter-regional priors to synthesize virtual signals for new subjects. Incorporating these virtual signals into motor decoders consistently outperforms decoding based on implanted electrodes alone. Our results demonstrate that the ECoG synthesis via generative AI framework effectively expands the functional field of view of intracranial interfaces, providing a non-surgical pathway to robust ECoG-based BCIs. Biological sciences/Neuroscience/Motor control Biological sciences/Neuroscience/Motor control/Brain–machine interface Biological sciences/Neuroscience/Computational neuroscience/Neural decoding Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Supplementary Information 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. 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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-8872713","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594660249,"identity":"b4a75b8a-c246-4051-933a-026152ff818b","order_by":0,"name":"June Sic 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