Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00365748" target="_blank" >RIV/67985807:_____/11:00365748 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NaBIC.2011.6089732" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2011.6089732</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NaBIC.2011.6089732" target="_blank" >10.1109/NaBIC.2011.6089732</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation

Popis výsledku v původním jazyce

The performance of the Common Tensor Discriminant Analysis CTDA method for Brain-Computer Interface EEG pattern classification is compared with three other classifiers. The classifiers are designed with the aim to distinguish EEG patterns appearing as aresult of performance of several mental tasks. Classifier comparison has yielded quite similar results as regards our experimental imagery movement data set as well as for BCI Competition IV data set. The Bayesian and Multiclass Common Spatial Patterns classifiers, which use solely interchannel covariance as input, are shown to be comparable in performance, while lagging behind the Multiclass Common Spatial Patterns classifier and the CTDA classifier, that is classifiers which additionally account for EEG frequency structure. It is shown that the CTDA classifier and the Multiclass Common Spatial Patterns classifier provide significantly better classification than other two methods but at a higher computational cost.

Název v anglickém jazyce

Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation

Popis výsledku anglicky

The performance of the Common Tensor Discriminant Analysis CTDA method for Brain-Computer Interface EEG pattern classification is compared with three other classifiers. The classifiers are designed with the aim to distinguish EEG patterns appearing as aresult of performance of several mental tasks. Classifier comparison has yielded quite similar results as regards our experimental imagery movement data set as well as for BCI Competition IV data set. The Bayesian and Multiclass Common Spatial Patterns classifiers, which use solely interchannel covariance as input, are shown to be comparable in performance, while lagging behind the Multiclass Common Spatial Patterns classifier and the CTDA classifier, that is classifiers which additionally account for EEG frequency structure. It is shown that the CTDA classifier and the Multiclass Common Spatial Patterns classifier provide significantly better classification than other two methods but at a higher computational cost.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Nature and Biologically Inspired Computing

ISBN

978-1-4577-1122-0

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

614-620

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Salamanca

Datum konání akce

19. 10. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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