{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-riemannian-geometry-for-ssvep-based","title":"Using Riemannian geometry for SSVEP-based Brain Computer Interface","arxiv_id":"1501.03227","date":"2015-01-14","proceeding":null,"authors":["Emmanuel K. Kalunga","Sylvain Chevallier","Quentin Barthelemy"],"abstract":"Riemannian geometry has been applied to Brain Computer Interface (BCI) for\nbrain signals classification yielding promising results. Studying\nelectroencephalographic (EEG) signals from their associated covariance matrices\nallows a mitigation of common sources of variability (electronic, electrical,\nbiological) by constructing a representation which is invariant to these\nperturbations. While working in Euclidean space with covariance matrices is\nknown to be error-prone, one might take advantage of algorithmic advances in\ninformation geometry and matrix manifold to implement methods for Symmetric\nPositive-Definite (SPD) matrices. This paper proposes a comprehensive review of\nthe actual tools of information geometry and how they could be applied on\ncovariance matrices of EEG. In practice, covariance matrices should be\nestimated, thus a thorough study of all estimators is conducted on real EEG\ndataset. As a main contribution, this paper proposes an online implementation\nof a classifier in the Riemannian space and its subsequent assessment in\nSteady-State Visually Evoked Potential (SSVEP) experimentations.","url_abs":"http://arxiv.org/abs/1501.03227v3","url_pdf":"http://arxiv.org/pdf/1501.03227v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"using-riemannian-geometry-for-ssvep-based","repo_url":"https://github.com/emmanuelkalunga/Online-SSVEP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"using-riemannian-geometry-for-ssvep-based","repo_url":"https://github.com/emmanuelkalunga/Offline-Riemannian-SSVEP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"ssvep","task_name":"SSVEP"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}