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Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces

Kumar, Satyam; Yger, Florian; Lotte, Fabien (2019), Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces, in Lee, Seong-Whan; Müller, Klaus-Robert, 2019 7th International Winter Conference on Brain-Computer Interface (BCI), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ. 10.1109/IWW-BCI.2019.8737349

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Winter_Conference_BCI.pdf (241.6Kb)
Type
Communication / Conférence
Date
2019
Conference title
7th International Winter Conference on Brain-Computer Interface (BCI)
Conference date
2019-02
Conference city
High 1 Resort
Conference country
"Korea
Book title
2019 7th International Winter Conference on Brain-Computer Interface (BCI)
Book author
Lee, Seong-Whan; Müller, Klaus-Robert
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
ISBN
978-1-5386-8116-9
Publication identifier
10.1109/IWW-BCI.2019.8737349
Metadata
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Author(s)
Kumar, Satyam
autre
Yger, Florian cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Lotte, Fabien cc
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Abstract (EN)
The omnipresence of non-stationarity and noise in Electroencephalogram signals restricts the ubiquitous use of Brain-Computer interface. One of the possible ways to tackle this problem is to adapt the computational model used to detect and classify different mental states. Adapting the model will possibly help us to track the changes and thus reducing the effect of non-stationarities. In this paper, we present different adaptation strategies for state of the art Riemannian geometry based classifiers. The offline evaluation of our proposed methods on two different datasets showed a statistically significant improvement over baseline non-adaptive classifiers. Moreover, we also demon- strate that combining different (hybrid) adaptation strategies generally increased the performance over individual adaptation schemes. Also, the improvement in average classification accuracy for a 3-class mental imagery BCI with hybrid adaption is as high as around 17% above the baseline non-adaptive classifier.
Subjects / Keywords
Riemannian Geometry; BCI; Adaptive classifier

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