New algorithm for EEG and EMG Separation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00230060" target="_blank" >RIV/68407700:21230/15:00230060 - isvavai.cz</a>

Výsledek na webu

<a href="http://radio.feld.cvut.cz/conf/poster2015/" target="_blank" >http://radio.feld.cvut.cz/conf/poster2015/</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

New algorithm for EEG and EMG Separation

Popis výsledku v původním jazyce

The paper presents newly proposed algorithm for the blind separation of EEG and EMG sources measured by high density electrode arrays. The algorithm is based on the maximization of the variance of variances of filtered principal components. Utilized highpass filter was optimized in order to extract the information which is used by the gradient algorithm to separate EEG and EMG components. The performance of the algorithm was evaluated by its use for the muscular artifacts removal. Present muscular artifacts were extracted from the estimated components with the use of the previously used classifier. It is compared with other similar approaches and it is shown that the suggested algorithm achieves higher quality of the processed EEG signal especially inthe case of strong muscular artifacts and is therefore useful for the preprocessing of the EEG records contaminated with the muscle activity.

Název v anglickém jazyce

New algorithm for EEG and EMG Separation

Popis výsledku anglicky

The paper presents newly proposed algorithm for the blind separation of EEG and EMG sources measured by high density electrode arrays. The algorithm is based on the maximization of the variance of variances of filtered principal components. Utilized highpass filter was optimized in order to extract the information which is used by the gradient algorithm to separate EEG and EMG components. The performance of the algorithm was evaluated by its use for the muscular artifacts removal. Present muscular artifacts were extracted from the estimated components with the use of the previously used classifier. It is compared with other similar approaches and it is shown that the suggested algorithm achieves higher quality of the processed EEG signal especially inthe case of strong muscular artifacts and is therefore useful for the preprocessing of the EEG records contaminated with the muscle activity.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 19th International Scientific Student Conferenece POSTER 2015

ISBN

978-80-01-05499-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Czech Technical University in Prague

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

14. 5. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—