ICA Model Order Estimation Using Clustering Method

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dc.contributor.authorRuckay, Lukas
dc.contributor.authorStastny, Jakub
dc.contributor.authorSovka, Pavel
dc.coverage.issue4cs
dc.coverage.volume16cs
dc.date.accessioned2016-03-24T06:45:00Z
dc.date.available2016-03-24T06:45:00Z
dc.date.issued2007-12cs
dc.description.abstractIn this paper a novel approach for independent component analysis (ICA) model order estimation of movement electroencephalogram (EEG) signals is described. The application is targeted to the brain-computer interface (BCI) EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs). The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA) for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level α = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.en
dc.formattextcs
dc.format.extent51-57cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2007, vol. 16, č. 4, s. 51-57. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/57324
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2007/07_04_051_057.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectEEG classificationen
dc.subjectbrain computer interfaceen
dc.subjectblind source separationen
dc.subjectindependent component analysisen
dc.subjectICA model orderen
dc.subjectclusteringen
dc.titleICA Model Order Estimation Using Clustering Methoden
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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