Pattern Rejection Strategies for the Design of Self-Paced EEG-based Brain-Computer Interfaces

Fabien Lotte 1, * Harold Mouchère 2 Anatole Lécuyer 1
* Corresponding author
1 BUNRAKU - Perception, decision and action of real and virtual humans in virtual environments and impact on real environments
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, ENS Cachan - École normale supérieure - Cachan, Inria Rennes – Bretagne Atlantique
2 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : This paper deals with pattern rejection strategies for self-paced Brain-Computer Interfaces (BCI). First, it introduces two pattern rejection strategies not used yet for self-paced BCI design: 1) the rejection class (RC) strategy and 2) thresholds on reliability functions (TRF) based on the automatic multiple-threshold learning algorithm. Second, it compares several rejection strategies using several classifiers, on motor imagery data, in order to identify their most desirable properties. Results showed that nonlinear classifiers led to the most efficient self-paced BCI. Concerning the reject option, RC outperformed a specialized reject classifier which outperformed TRF. Overall, the best results were obtained using the RC reject option and non-linear classifiers such as a Gaussian support vector machine, a fuzzy inference system or a radial basis function network.
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Fabien Lotte, Harold Mouchère, Anatole Lécuyer. Pattern Rejection Strategies for the Design of Self-Paced EEG-based Brain-Computer Interfaces. International Conference on Pattern Recognition, Dec 2008, Tampa, United States. ⟨inria-00310878⟩

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