Riemannian approaches in Brain-Computer Interfaces: a review
Identifieur interne : 000000 ( Main/Exploration ); suivant : 000001Riemannian approaches in Brain-Computer Interfaces: a review
Auteurs : Florian Yger [France] ; Maxime Berar [France] ; Fabien Lotte [France]Source :
- IEEE Transactions on Neural Systems and Rehabilitation Engineering [ 1534-4320 ] ; 2017.
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Abstract
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.
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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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Yger, Florian" sort="Yger, Florian" uniqKey="Yger F" first="Florian" last="Yger">Florian Yger</name></noRegion><name sortKey="Berar, Maxime" sort="Berar, Maxime" uniqKey="Berar M" first="Maxime" last="Berar">Maxime Berar</name><name sortKey="Lotte, Fabien" sort="Lotte, Fabien" uniqKey="Lotte F" first="Fabien" last="Lotte">Fabien Lotte</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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