{"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/from-euclidean-to-riemannian-means","title":"From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification","arxiv_id":null,"date":"2016-04-03","proceeding":"Geometric Science of Information 2016 4","authors":["Emmanuel Kalunga","Sylvain Chevallier","Quentin Barthélemy","Karim Djouani","Yskandar Hamam","Eric Monacelli"],"abstract":"Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing. Most of the state-of-the-art approaches deal with covariance matrices , and indeed Riemannian geometry has provided a substantial framework for developing new algorithms. Most notably , a straightforward algorithm such as Minimum Distance to Mean yields competitive results when applied with a Riemannian distance. This applicative contribution aims at assessing the impact of several distances on real EEG dataset , as the invariances embedded in those distances have an influence on the classification accuracy . Euclidean and Riemannian distances and means are compared both in term of quality of results and of computational load .","url_abs":"https://hal.archives-ouvertes.fr/hal-01351753","url_pdf":"https://hal.archives-ouvertes.fr/hal-01351753/document","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":"from-euclidean-to-riemannian-means","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":"classification-1","task_name":"Classification"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"classification","task_name":"General Classification"},{"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}