Supervised neuronal approaches for EEG signal classification: experimental studies

Frédéric Alexandre 1 Nizar Kerkeni 1 Khaled Ben Khalifa 2 Mohamed Hédi Bedoui 2 Laurent Bougrain 1 Mohamed Dogui 3
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 TIM - Technologie et Imgerie Médicale
3 Service d'exploration fonctionnelle du système nerveux
Abstract : Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nature of data. Such signals are complex and difficult to process. The second class of reasons refers to the nature of underlying knowledge. Expertise is manifold and difficult to formalize and to be made compatible with a numerical processing. In previous studies we have deeply described that expertise and explained, from theoretical and bibliographical studies, why artificial neural networks could be interesting candidates to perform such a signal interpretation. In this paper, we report recent experiments that we have made on real EEG data in a classification framework. These results are interesting with regard to the state of the art. They also indicate that further work must be done on expertise integration in our neuronal platform.
Keywords : neural networks self-organizing maps multi-layer perceptrons electroencephalographic signal interpretation medical application
Type de document :
Communication dans un congrès
Angel Pasqual del Pobil. The 10th IASTED International Conference on Artificial Intelligence and Soft Computing - ASC 2006, Aug 2006, Palma de Mallorca/Spain, Acta Press, 2006
Liste complète des métadonnées

Littérature citée [7 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/inria-00103248
Contributeur : Frédéric Alexandre <>
Soumis le : mardi 3 octobre 2006 - 17:28:38
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48
Document(s) archivé(s) le : mardi 6 avril 2010 - 17:53:35

Fichier

544-075.pdf

Identifiants

  • HAL Id : inria-00103248, version 1

Collections

INRIA | LORIA | UNIV-LORRAINE

Citation

Frédéric Alexandre, Nizar Kerkeni, Khaled Ben Khalifa, Mohamed Hédi Bedoui, Laurent Bougrain, et al.. Supervised neuronal approaches for EEG signal classification: experimental studies. Angel Pasqual del Pobil. The 10th IASTED International Conference on Artificial Intelligence and Soft Computing - ASC 2006, Aug 2006, Palma de Mallorca/Spain, Acta Press, 2006. 〈inria-00103248〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

468

Téléchargements de fichiers

389

Contact


archive-ouverte@inria.fr