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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F12%3A43916280%21RIV13-MSM-23520___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
This paper deals with a suitable method for decomposition of EEG/ERP signal to waveforms which are grouped is such way that one or few groups contain ERP P3 waveforms. At the beginning, the EEG/ERP domain is briefly introduced and essential information about EEG and ERP signals is given. Then, the method for waveforms grouping based on matching pursuit algorithm with Gabor dictionary as a preprocessing method for feature extraction for ART2 neural network is explained in detail. Emphasis is placed on selection of suitable feature extraction method. Comparison of tested feature extraction methods and summarization is given at the end. This paper deals with a suitable method for decomposition of EEG/ERP signal to waveforms which are grouped is such way that one or few groups contain ERP P3 waveforms. At the beginning, the EEG/ERP domain is briefly introduced and essential information about EEG and ERP signals is given. Then, the method for waveforms grouping based on matching pursuit algorithm with Gabor dictionary as a preprocessing method for feature extraction for ART2 neural network is explained in detail. Emphasis is placed on selection of suitable feature extraction method. Comparison of tested feature extraction methods and summarization is given at the end.
dcterms:title
Using ART2 for Clustering of Gabor Atoms Describing ERP P3 Waveforms Using ART2 for Clustering of Gabor Atoms Describing ERP P3 Waveforms
skos:prefLabel
Using ART2 for Clustering of Gabor Atoms Describing ERP P3 Waveforms Using ART2 for Clustering of Gabor Atoms Describing ERP P3 Waveforms
skos:notation
RIV/49777513:23520/12:43916280!RIV13-MSM-23520___
n6:predkladatel
n18:orjk%3A23520
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n20:S
n3:aktivity
S
n3:dodaniDat
n4:2013
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n9:5850274 n9:2152363
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n17:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
176486
n3:idVysledku
RIV/49777513:23520/12:43916280
n3:jazykVysledku
n14:eng
n3:klicovaSlova
EEG; electroencephalography; P3 component; ERP; event-related potential; MP; matching pursuit algorithm; clustering; feature vector; ANN; adaptive resonance theory neural network; ART2; signal energy; Gabor atoms
n3:klicoveSlovo
n7:ANN n7:adaptive%20resonance%20theory%20neural%20network n7:MP n7:clustering n7:ART2 n7:electroencephalography n7:P3%20component n7:Gabor%20atoms n7:event-related%20potential n7:signal%20energy n7:ERP n7:matching%20pursuit%20algorithm n7:feature%20vector n7:EEG
n3:kontrolniKodProRIV
[B6B8497C5BE6]
n3:mistoKonaniAkce
Chongqing, Čína
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
BMEI 2012
n3:obor
n11:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n4:2012
n3:tvurceVysledku
Mautner, Pavel Řondík, Tomáš
n3:typAkce
n15:WRD
n3:zahajeniAkce
2012-10-16+02:00
s:numberOfPages
4
n16:hasPublisher
IEEE
n12:isbn
978-1-4673-1182-3
n21:organizacniJednotka
23520