FuRIA : un nouvel algorithme d’extraction de caractéristiques pour les interfaces cerveauordinateur utilisant modèles inverses et modèles flous - FuRIA: a new feature extraction algorithm for braincomputer interfaces using fuzzy and inverse models

FuRIA : un nouvel algorithme d’extraction de caractéristiques pour les interfaces cerveauordinateur utilisant modèles inverses et modèles flous

FuRIA: a new feature extraction algorithm for braincomputer interfaces using fuzzy and inverse models

Fabien Lotte Anatole Lécuyer  Bruno Arnaldi 

IRISA-INSA Rennes, 20 avenue des buttes de coesmes, 35043 Rennes Cedex, France

IRISA-INRIA Rennes, avenue du général Leclerc, Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France

Corresponding Author Email: 
15 April 2008
31 August 2008
| Citation



When using Brain-Computer Interfaces (BCI) based on ElectroEncephaloGraphy (EEG), the identification of mental tasks relies on two main points: feature extraction and classification [MAM+06, BFWB07, LCL+07]. Feature extraction aims at describing EEG signals by a few relevant values called “features”, whereas classification aims at automatically assigning a class to these features. In this paper we focus on feature extraction, as the BCI community has stressed the need to explore new feature extraction algorithms [MAM+06].

Recently, inverse models have been revealed as promising feature extraction algorithms for BCI [QDH04, GGP+05, WGWB05, CLL06]. Such models aims at computing the activity in the whole brain volume, by using only scalp EEG signals and a head model representing the brain as a set of voxels (volume elements). The activity computed in a few brain regions has been used as features for BCI systems.

Despite good results, some limitations remain. Indeed, it seems that current methods cannot conciliate genericity, i.e., the capability to deal with any kind of mental task, and the fact of generating few features. On one hand, methods that are generic and automatic tend to generate a large number of features, as they extract several features for each voxel [GGP+05]. The activity in neighboring voxels can be correlated and, as such, it would be more appropriate to gather these voxels in brain regions. On the other hand, methods that generate few features have been proposed, but they are not generic anymore as they need a priori knowledge on the mental tasks used, and are currently limited to motor imagerybased BCI [QDH04, WGWB05]. Recently, we have proposed a method which is generic and which generates few features, as voxels whose activity is correlated are gathered into Regions Of Interest (ROI) [CLL06]. However, this method is not completely automatic and is limited to the use of two ROI whose spatial extension is hard to define [CLL06].

In this paper, we propose a generic feature extraction algorithm which can automatically identify any number of relevant ROI and can properly define their spatial extension thanks to the new concept of fuzzy ROI. This algorithm is known as FuRIA, which stands for “Fuzzy Region of Interest Activity”.


Cet article propose un nouvel algorithme d’extraction de caractéristiques pour les Interfaces CerveauOrdinateur (ICO) basées sur l’électroencéphalographie. Cet algorithme utilise les modèles inverses ainsi que le nouveau concept de Région d’Intérêt (RI) floue. Il peut automatiquement identifier les RI pertinentes pour la discrimination ainsi que les bandes de fréquences dans lesquelles ces RI sont les plus discriminantes. Les activités calculées dans ces RI peuvent ensuite être utilisées comme caractéristiques d’entrée pour n’importe quel classifieur. Une première évaluation de l’algorithme, utilisant une Machine à Vecteurs Supports (SVM) comme classifieur, est présentée sur le jeu de données IV de la «BCI competition 2003». Les résultats s’avèrent prometteurs avec une précision sur l’ensemble de test allant de 85% à 86% contre 84% pour le gagnant de la compétition sur ces données. Enfin, nous montrons que combiner ce nouvel algorithme avec des systèmes d’inférence flous permet de concevoir des ICO potentiellement interprétables.


brain-computer interface (BCI), feature extraction, inverse model, source localization, classification, interpretability, fuzzy set, electroencephalography (EEG).

Mots clés

interface cerveau-ordinateur (ICO), extraction de caractéristiques, modèle inverse, localisation de sources, classification, interprétabilité, ensemble flou, électroencéphalographie (EEG).

1. Introduction
2. Interfaces Cerveauordinateur Et Modèles Inverses : Un État-De-L’art
3. L’algorithme FuRIA
4. Implémentation De L’algorithme
5. Évaluation
6. Conclusion Et Travaux Futurs

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