Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092436" target="_blank" >RIV/61989100:27240/14:86092436 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092436

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_19" target="_blank" >http://dx.doi.org/10.1007/978-3-319-08156-4_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_19" target="_blank" >10.1007/978-3-319-08156-4_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)

Popis výsledku v původním jazyce

Electrical brain activity in subjects controlling Brain-Computer Interface (BCI) based on motor imagery is studied. A used data set contains 7440 observations corresponding to distributions of electrical potential at the head surface obtained by Independent Component Analysis of 155 48-channel EEG recordings over 16 subjects. The distributions are interpreted as produced by the current dipolar sources inside the head. To reveal the sources of electrical brain activity the most typical for motor imagery,the corresponding ICA components were clustered by Attractor Neural Network with Increasing Activity (ANNIA). ANNIA was already successfully applied to clustering textual documents and genome data. Among the expected clusters of components (blinks and mu-rhythm ERD) the ones reflecting the frontal and occipital cortex activity were also extracted. Although the cluster analysis can not substitute careful data examination and interpretation however it is a useful pre-processing step which

Název v anglickém jazyce

Clustering the Sources of EEG Activity during Motor Imagery by Attractor Neural Network with Increasing Activity (ANNIA)

Popis výsledku anglicky

Electrical brain activity in subjects controlling Brain-Computer Interface (BCI) based on motor imagery is studied. A used data set contains 7440 observations corresponding to distributions of electrical potential at the head surface obtained by Independent Component Analysis of 155 48-channel EEG recordings over 16 subjects. The distributions are interpreted as produced by the current dipolar sources inside the head. To reveal the sources of electrical brain activity the most typical for motor imagery,the corresponding ICA components were clustered by Attractor Neural Network with Increasing Activity (ANNIA). ANNIA was already successfully applied to clustering textual documents and genome data. Among the expected clusters of components (blinks and mu-rhythm ERD) the ones reflecting the frontal and occipital cortex activity were also extracted. Although the cluster analysis can not substitute careful data examination and interpretation however it is a useful pre-processing step which

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 237

ISBN

978-3-319-01780-8

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

9

Strana od-do

183-191

Název nakladatele

Springer

Místo vydání

Basel

Místo konání akce

Ostrava

Datum konání akce

22. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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