The Riemannian Geometry of User Learning in MI-BCI: a Cybathlon Longitudinal Study

The Riemannian Geometry of User Learning in MI-BCI: a Cybathlon Longitudinal Study | 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 The Riemannian Geometry of User Learning in MI-BCI: a Cybathlon Longitudinal Study Alessio Palatella, Iustin Curcean, Francesco Bettella, Emanuele Menegatti, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8959985/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background: \Ac{mi} \acp{bci} are promising assistive technologies, however, their development is hindered by low \ac{eeg} signal quality and an incomplete understanding of neural modulation during training. Traditional performance-based metrics provide limited insight into the mechanisms of skill acquisition. We hypothesize that Riemannian geometry offers a robust framework for analyzing structural and physiological \ac{eeg} patterns associated with learning. Methods: This study analyzes longitudinal \ac{eeg} data collected during a Cybathlon pilot to investigate how Riemannian features evolve throughout \ac{mi}-\ac{bci} training. Novel metrics are introduced by combining geodesic distances on the Riemannian manifold with cosine similarity between tangent-space vectors, enabling the quantification of neural trajectories during training. Results: The results demonstrate that the proposed Riemannian features capture meaningful changes in neural representations that reflect user learning. Furthermore, we introduce the concept of user learning states, showing that specific subsets of the extracted neural features can be associated with a emergent or with a stable state Conclusions: These findings highlight the value of geometric \ac{eeg} metrics for characterizing cortical adaptation during \ac{mi}-\ac{bci} training and for guiding the development of adaptive training strategies in \ac{mi}-based \ac{bci} systems. User learning Riemannian geometry Motor imagery Brain Computer Interface Cybathlon Full Text Additional Declarations No competing interests reported. Supplementary Files EEGgeometryinRiemannianmanifoldssupplementary.zip Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 08 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 24 Feb, 2026 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. 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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-8959985","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604455126,"identity":"1685ac63-d0c2-452a-b261-ea30f9f7328f","order_by":0,"name":"Alessio Palatella","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Palatella","suffix":""},{"id":604455127,"identity":"21989918-5345-4997-9d63-c806dceb4a6c","order_by":1,"name":"Iustin Curcean","email":"","orcid":"","institution":"Technical University of 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