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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

Type
Article accepté pour publication ou publié
Date
2017
Journal name
IEEE Transactions on Neural System and Rehabilitation Engineering
Number
99
Publication identifier
10.1109/TNSRE.2016.2627016
Metadata
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Author(s)
Yger, Florian cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Berar, Maxime
Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes [LITIS]
Lotte, Fabien cc
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 / Keywords
Brain-Computer Interface (BCI); Riemannian geometry; covariance matrices; subspaces; source extraction; Electroencephalography (EEG); classification

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