M
Marco Congedo
Researcher at University of Grenoble
Publications - 160
Citations - 10647
Marco Congedo is an academic researcher from University of Grenoble. The author has contributed to research in topics: Blind signal separation & Riemannian geometry. The author has an hindex of 39, co-authored 155 publications receiving 8610 citations. Previous affiliations of Marco Congedo include Centre national de la recherche scientifique & French Institute for Research in Computer Science and Automation.
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Journal ArticleDOI
A review of classification algorithms for EEG-based brain–computer interfaces
TL;DR: This paper compares classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG) in terms of performance and provides guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update
Fabien Lotte,Laurent Bougrain,Andrzej Cichocki,Andrzej Cichocki,Maureen Clerc,Marco Congedo,Alain Rakotomamonjy,Florian Yger +7 more
TL;DR: A comprehensive overview of the modern classification algorithms used in EEG-based BCIs is provided, the principles of these methods and guidelines on when and how to use them are presented, and a number of challenges to further advance EEG classification in BCI are identified.
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Openvibe: An open-source software platform to design, test, and use brain--computer interfaces in real and virtual environments
Yann Renard,Fabien Lotte,Guillaume Gibert,Marco Congedo,Emmanuel Maby,Vincent Delannoy,Olivier F. Bertrand,Anatole Lécuyer +7 more
TL;DR: The OpenViBE software platform is described which enables researchers to design, test, and use braincomputer interfaces (BCIs) and its suitability for the design of VR applications controlled with a BCI is shown.
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Multiclass Brain–Computer Interface Classification by Riemannian Geometry
TL;DR: A new classification framework for brain-computer interface (BCI) based on motor imagery using spatial covariance matrices as EEG signal descriptors and relying on Riemannian geometry to directly classify these matrices using the topology of the manifold of symmetric and positive definite matrices.
Journal ArticleDOI
The neural correlates of tinnitus-related distress
Sven Vanneste,Mark Plazier,Elsa van der Loo,Paul Van de Heyning,Marco Congedo,Dirk De Ridder +5 more
TL;DR: Results show more synchronized alpha activity in the tinnitus patients with a serious amount of distress with peaks localized to various emotion-related areas, and areas found show some overlap with the emotional component of the pain matrix and the distress related areas in asthmatic dyspnea.