FuRIA: A Novel Feature Extraction Algorithm for Brain-Computer Interfaces using Inverse Models and Fuzzy Regions of Interest

Fabien Lotte 1, * Anatole Lécuyer 1 Bruno Arnaldi 1
* Auteur correspondant
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 : In this paper, we propose a new feature extraction algorithm for Brain-Computer Interfaces (BCIs). This algorithm is based on inverse models and uses the novel concept of fuzzy Region Of Interest (ROI). It can automatically identify the relevant ROIs and their reactive frequency bands. The activity in these ROIs can be used as features for any classifier. A first evaluation of the algorithm, using a Support Vector Machine (SVM) as classifier, is reported on data set IV from BCI competition 2003. Results are promising as we reached an accuracy on the test set ranging from 85% to 86% whereas the winner of the competition on this data set reached 84%.
Keywords : feature extraction fuzzy models sLORETA classification Brain-Computer interface inverse model fuzzy sets
Type de document :
Communication dans un congrès
3rd IEEE-EMBS international conference on neural engineering, May 2007, Hawaii - Kohala Coast, United States. 2007
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Fabien Lotte, Anatole Lécuyer, Bruno Arnaldi. FuRIA: A Novel Feature Extraction Algorithm for Brain-Computer Interfaces using Inverse Models and Fuzzy Regions of Interest. 3rd IEEE-EMBS international conference on neural engineering, May 2007, Hawaii - Kohala Coast, United States. 2007. 〈inria-00134961〉

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