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- W383121320 abstract "When using Brain-Computer Interfaces (BCI) based on ElectroEncephaloGraphy (EEG), the identification of mental tasksrelies on two main points: feature extraction and classification [MAM+06, BFWB07, LCL+07]. Feature extraction aims atdescribing EEG signals by a few relevant values called “features ”, whereas classification aims at automaticallyassigning a class to these features. In this paper we focus on feature extraction, as the BCI community has stressed theneed 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 EEGsignals and a head model representing the brain as a set of voxels (volume elements). The activity computed in a fewbrain 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., thecapability to deal with any kind of mental task, and the fact of generating few features. On one hand, methods that aregeneric 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 thesevoxels in brain regions. On the other hand, methods that generate few features have been proposed, but they are notgeneric anymore as they need a priori knowledge on the mental tasks used, and are currently limited to motor imagerybasedBCI [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 notcompletely 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 relevantROI and can properly define their spatial extension thanks to the new concept of fuzzy ROI. This algorithm is known asFuRIA, which stands for “Fuzzy Region of Interest Activity”..." @default.
- W383121320 created "2016-06-24" @default.
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- W383121320 date "2008-01-01" @default.
- W383121320 modified "2023-09-23" @default.
- W383121320 title "FuRIA : un nouvel algorithme d’extraction de caractéristiques pour les interfaces cerveau-ordinateur et modèles flous" @default.
- W383121320 hasPublicationYear "2008" @default.
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