Artificial Neural Networks to extract Knowledge from EEG
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Auteurs : Frédéric Alexandre ; Nizar Kerkeni ; Khaled Ben Khalifa ; Mohamed Hédi BédouiSource :
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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.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="205">Artificial Neural Networks to extract Knowledge from EEG</title></titleStmt><publicationStmt><idno type="RBID">CRIN:alexandre05a</idno><date when="2005" year="2005">2005</date><idno type="wicri:Area/Crin/Corpus">004016</idno><idno type="wicri:Area/Crin/Curation">004016</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">004016</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Artificial Neural Networks to extract Knowledge from EEG</title><author><name sortKey="Alexandre, Frederic" sort="Alexandre, Frederic" uniqKey="Alexandre F" first="Frédéric" last="Alexandre">Frédéric Alexandre</name></author><author><name sortKey="Kerkeni, Nizar" sort="Kerkeni, Nizar" uniqKey="Kerkeni N" first="Nizar" last="Kerkeni">Nizar Kerkeni</name></author><author><name sortKey="Ben Khalifa, Khaled" sort="Ben Khalifa, Khaled" uniqKey="Ben Khalifa K" first="Khaled" last="Ben Khalifa">Khaled Ben Khalifa</name></author><author><name sortKey="Bedoui, Mohamed Hedi" sort="Bedoui, Mohamed Hedi" uniqKey="Bedoui M" first="Mohamed Hédi" last="Bédoui">Mohamed Hédi Bédoui</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>artificial neural networks</term><term>data representation and visualization</term><term>eeg interpretation</term><term>signal processing</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="754">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.</div></front></TEI><BibTex type="inproceedings"><ref>alexandre05a</ref><crinnumber>A05-R-004</crinnumber><category>3</category><equipe>CORTEX</equipe><author><e>Alexandre, Frédéric</e><e>Kerkeni, Nizar</e><e>Ben Khalifa, Khaled</e><e>Bédoui, Mohamed Hédi</e></author><title>Artificial Neural Networks to extract Knowledge from EEG</title><booktitle>{The IASTED International Conference on Biomedical Engineering - BioMED2005, Innsbruck, Austria}</booktitle><year>2005</year><month>Feb</month><keywords><e>signal processing</e><e>data representation and visualization</e><e>eeg interpretation</e><e>artificial neural networks</e></keywords><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.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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