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

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Winter_Conference_BCI.pdf (241.6Kb)
Date
2019
Dewey
Intelligence artificielle
Sujet
Riemannian Geometry; BCI; Adaptive classifier
DOI
http://dx.doi.org/10.1109/IWW-BCI.2019.8737349
Conference name
7th International Winter Conference on Brain-Computer Interface (BCI)
Conference date
02-2019
Conference city
High 1 Resort
Conference country
"Korea
Book title
2019 7th International Winter Conference on Brain-Computer Interface (BCI)
Author
Lee, Seong-Whan; Müller, Klaus-Robert
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Publisher city
Piscataway, NJ
Year
2019
ISBN
978-1-5386-8116-9
URI
https://basepub.dauphine.fr/handle/123456789/20057
Collections
  • LAMSADE : Publications
Metadata
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Author
Kumar, Satyam
115536 autre
Yger, Florian
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Lotte, Fabien
3102 Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Type
Communication / Conférence
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.

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