Application of neutral network by EEG signal classification

dc.contributor.authorGála, Michal
dc.contributor.authorMohylová, Jitka
dc.contributor.authorKrajča, Vladimír
dc.date.accessioned2011-02-09T14:37:11Z
dc.date.available2011-02-09T14:37:11Z
dc.date.issued2008
dc.description.abstractAnalysis of long-term EEG requires that it is segmented into piece-wise stationary sections and classified. Neural network architecture is introduced for the problem of classification of EEG signals. This paper deals with basic signal classification into two classes. This work is a ground towards creating an algorithm to sleep status analysis. Signal is first worked by signal segmentation and then is used a neural network to classification into two class.en
dc.format.extent299087 bytescs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAdvances in electrical and electronic engineering. 2008, vol. 7, no. 1, 2, p. 346-349.en
dc.identifier.issn1336-1376
dc.identifier.urihttp://hdl.handle.net/10084/84206
dc.language.isoenen
dc.publisherŽilinská univerzita v Žiline. Elektrotechnická fakultaen
dc.relation.ispartofseriesAdvances in electrical and electronic engineeringen
dc.relation.urihttp://advances.utc.sk/index.php/AEEEen
dc.rightsCreative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.rights© Žilinská univerzita v Žiline. Elektrotechnická fakulta
dc.rights.accessopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.titleApplication of neutral network by EEG signal classificationen
dc.typearticleen
dc.type.statusPeer-reviewedcs
dc.type.versionpublishedVersioncs

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