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| rdf:type
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| Description
| - Příspěvek popisuje nový přístup pro odhad řádu ICA modelu (independent component analysis) pohybového EEG (electroencephalogram). Aplikace je zaměřena na předzpracování EEG pro rozhraní mozek-počítač (brain-computer interface, BCI). Výběr pouze pohybových komponent (IC) může vést ke zvýšení klasifikačního skóre EEG v BCI systému. Reálný počet nezávislých zdrojů v mozku je důležitým parametrem pro předzpracování. První odhad počtu nezávislých zdrojů byl proveden pomocí PCA (principal component analysis). Nicméně PCA odhaduje pouze počet nekorelovaných zdrojů a nedokáže odhadnout počet statisticky nezávislých zdrojů. V této práci jsem použili jiný přístup - výběr vysoce korelovaných IC z několika běhů ICA. Odhad řádu modelu ICA je proveden na hladině významnosti alpha = 0.05 a řád modelu je více či méně závislý na inicializaci ICA algoritmu. (cs)
- In 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 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 stimates 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 alpha = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.
- In 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 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 stimates 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 alpha = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters. (en)
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| Title
| - ICA Model Order Estimation Using Clustering Method
- ICA Model Order Estimation Using Clustering Method (en)
- Odhad řádu modelu ICA pomocí clusterovací metody (cs)
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| skos:prefLabel
| - ICA Model Order Estimation Using Clustering Method
- ICA Model Order Estimation Using Clustering Method (en)
- Odhad řádu modelu ICA pomocí clusterovací metody (cs)
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| skos:notation
| - RIV/68407700:21230/07:03135611!RIV08-GA0-21230___
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(GD102/03/H085), Z(MSM6840770012)
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03135611
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - EEG classification; blind source separation; brain computer interface; independent component analysis (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Sovka, Pavel
- Šťastný, Jakub
- Ručkay, Lukáš
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| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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| http://localhost/t...ganizacniJednotka
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