Riemannian approaches in Brain-Computer Interfaces: a review
Yger, Florian; Berar, Maxime; Lotte, Fabien (2017), Riemannian approaches in Brain-Computer Interfaces: a review, IEEE Transactions on Neural System and Rehabilitation Engineering, 99. 10.1109/TNSRE.2016.2627016
TypeArticle accepté pour publication ou publié
Journal nameIEEE Transactions on Neural System and Rehabilitation Engineering
MetadataShow full item record
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes [LITIS]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Abstract (EN)Although promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.
Subjects / KeywordsBrain-Computer Interface (BCI); Riemannian geometry; covariance matrices; subspaces; source extraction; Electroencephalography (EEG); classification
Showing items related by title and author.
Lotte, Fabien; Bougrain, Laurent; Cichocki, Andrzej; Clerc, Maureen; Congedo, Marco; Rakotomamonjy, Alain; Yger, Florian (2018) Article accepté pour publication ou publié
Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results Yamamoto, Maria Sayu; Lotte, Fabien; Yger, Florian; Chevallier, Sylvain (2022) Communication / Conférence