Skip to Main content Skip to Navigation
Conference papers

The Use of Fuzzy Inference Systems for Classification in EEG-based Brain-Computer Interfaces

Fabien Lotte 1
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
Abstract : This paper introduces the use of a Fuzzy Inference System (FIS) for classification in EEG-based Brain-Computer Interfaces (BCI) systems. We present our FIS algorithm and compare it, on motor imagery signals, with three other popular classifiers, widely used in the BCI community. Our results show that FIS outperformed a Linear Classifier and reached the same level of accuracy as Support Vector Machine and neural networks. Thus, FIS-based classification is suitable for BCI design. Furthermore, FIS algorithms have two additionnal advantages: they are readable and easily extensible.
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Fabien Lotte Connect in order to contact the contributor
Submitted on : Tuesday, March 6, 2007 - 9:35:55 AM
Last modification on : Monday, February 15, 2021 - 10:38:26 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 10:36:52 PM


Files produced by the author(s)


  • HAL Id : inria-00134951, version 1


Fabien Lotte. The Use of Fuzzy Inference Systems for Classification in EEG-based Brain-Computer Interfaces. 3rd International Brain-Computer Interfaces Workshop and Training Course, Sep 2006, Graz, Austria. ⟨inria-00134951⟩



Record views


Files downloads