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Supervised neuronal approaches for EEG signal classification: experimental studies

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.
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https://hal.inria.fr/inria-00103248
Contributor : Frédéric Alexandre <>
Submitted on : Tuesday, October 3, 2006 - 5:28:38 PM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 5:53:35 PM

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

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