Removal of Muscular Artifacts from EEG Records

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00217953" target="_blank" >RIV/68407700:21230/14:00217953 - isvavai.cz</a>

Výsledek na webu

<a href="http://radio.feld.cvut.cz/conf/poster2014/" target="_blank" >http://radio.feld.cvut.cz/conf/poster2014/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Removal of Muscular Artifacts from EEG Records

Popis výsledku v původním jazyce

The paper presents the algorithm for the removal of muscular artifacts from EEG signals recorded with the high density EEG electrode array. The proposed approach combines the blind source separation algorithm based on the canonical correlation analysis (CCA), the classifier based on three independent Bayesian classifiers and the regression method. In order to estimate the present muscular artifacts, the CCA is applied on electrodes located on the edge of the measuring array. Present muscular artifacts are then extracted from the estimated separated components with the use of the classifier. The last step of the algorithm involves the scaling of each muscular artifact and their subtraction from each EEG signal, so that they are no longer correlated witheach other. The performance of the algorithm is evaluated for two cases. In the first case, the CCA is used as the BSS method, in the second case the Second Order Blind Identification (SOBI) method is used for comparison purposes. It is

Název v anglickém jazyce

Removal of Muscular Artifacts from EEG Records

Popis výsledku anglicky

The paper presents the algorithm for the removal of muscular artifacts from EEG signals recorded with the high density EEG electrode array. The proposed approach combines the blind source separation algorithm based on the canonical correlation analysis (CCA), the classifier based on three independent Bayesian classifiers and the regression method. In order to estimate the present muscular artifacts, the CCA is applied on electrodes located on the edge of the measuring array. Present muscular artifacts are then extracted from the estimated separated components with the use of the classifier. The last step of the algorithm involves the scaling of each muscular artifact and their subtraction from each EEG signal, so that they are no longer correlated witheach other. The performance of the algorithm is evaluated for two cases. In the first case, the CCA is used as the BSS method, in the second case the Second Order Blind Identification (SOBI) method is used for comparison purposes. It is

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

POSTER 2014 - 18th International Student Conference on Electrical Engineering

ISBN

978-80-01-05499-4

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

15. 5. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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