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Artificial Neural Networks to extract Knowledge from EEG

Frédéric Alexandre 1 Nizar Kerkeni 1 Khaled Ben Khalifa Mohamed Hédi Bédoui
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : EEG signals are very difficult to interpret because they are dynamic, non-linear and non-stationary signals. Human expertise also indicates that multi-level analysis must be performed to integrate various sources of knowledge. In this paper, we review these difficulties and propose that artificial neural networks could be good candidates to handle such a difficult problem.
keyword : signal processing data representation and visualization eeg interpretation artificial neural networks
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Conference papers
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https://hal.inria.fr/inria-00001258
Contributor : Agnès Vidard <>
Submitted on : Tuesday, April 18, 2006 - 2:46:21 PM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM

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  • HAL Id : inria-00001258, version 1

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INRIA | CNRS | LORIA | UNIV-LORRAINE

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Frédéric Alexandre, Nizar Kerkeni, Khaled Ben Khalifa, Mohamed Hédi Bédoui. Artificial Neural Networks to extract Knowledge from EEG. The IASTED International Conference on Biomedical Engineering - BioMED2005, Feb 2005, Innsbruck/Austria. ⟨inria-00001258⟩

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