Comparison of four classification methods for brain-computer interface

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00359738" target="_blank" >RIV/67985807:_____/11:00359738 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of four classification methods for brain-computer interface

Popis výsledku v původním jazyce

Four classifiers effectiveness, for Brain Computer Interface (BCI) based on multichannel EEG with aim to distinguish EEG patterns corresponding to performance of several mental tasks, is compared. Basic Bayesian classifier (BC) exploits only inter-channel covariance matrices. The second one based on Bayesian approach exploits inter-channel covariance matrices estimated separately for several frequency bands (Multiband Bayesian Classifier, MBBC). The third one based on Multiclass Common Spatial Patterns(MSCP) method exploits only inter-channel covariance matrices as BC. The fourth one based on Common Tensor Discriminant Analysis (CTDA) takes EEG frequency structure into account. The MBBC and CTDA classifiers perform significantly better than the two other methods. Classifiers computational complexity analysis shows that an increase in the classifying quality is always accompanied by a significant increase of computational complexity.

Název v anglickém jazyce

Comparison of four classification methods for brain-computer interface

Popis výsledku anglicky

Four classifiers effectiveness, for Brain Computer Interface (BCI) based on multichannel EEG with aim to distinguish EEG patterns corresponding to performance of several mental tasks, is compared. Basic Bayesian classifier (BC) exploits only inter-channel covariance matrices. The second one based on Bayesian approach exploits inter-channel covariance matrices estimated separately for several frequency bands (Multiband Bayesian Classifier, MBBC). The third one based on Multiclass Common Spatial Patterns(MSCP) method exploits only inter-channel covariance matrices as BC. The fourth one based on Common Tensor Discriminant Analysis (CTDA) takes EEG frequency structure into account. The MBBC and CTDA classifiers perform significantly better than the two other methods. Classifiers computational complexity analysis shows that an increase in the classifying quality is always accompanied by a significant increase of computational complexity.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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 periodika

Neural Network World

ISSN

1210-0552

e-ISSN

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Svazek periodika

21

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

101-115

Kód UT WoS článku

000290838300001

EID výsledku v databázi Scopus

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