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Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA
R. Labounek, DA. Bridwell, R. Mareček, M. Lamoš, M. Mikl, T. Slavíček, P. Bednařík, J. Baštinec, P. Hluštík, M. Brázdil, J. Jan,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
ProQuest Central
od 1999-07-01 do Před 1 rokem
Medline Complete (EBSCOhost)
od 2009-05-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 1999-07-01 do Před 1 rokem
Psychology Database (ProQuest)
od 1999-07-01 do Před 1 rokem
- MeSH
- algoritmy MeSH
- analýza hlavních komponent MeSH
- elektroencefalografie metody statistika a číselné údaje MeSH
- interpretace obrazu počítačem metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku metody MeSH
- mladý dospělý MeSH
- počítačové zpracování signálu MeSH
- psychomotorický výkon fyziologie MeSH
- reprodukovatelnost výsledků MeSH
- rozhodování fyziologie MeSH
- shluková analýza MeSH
- zraková percepce fyziologie MeSH
- Check Tag
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.
Central European Institute of Technology Masaryk University Brno Czech Republic
Department of Biomedical Engineering Brno University of Technology Brno Czech Republic
Department of Mathematics Brno University of Technology Brno Czech Republic
Citace poskytuje Crossref.org
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- $a Labounek, René $u Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic. rene.labounek@gmail.com. Central European Institute of Technology, Masaryk University, Brno, Czech Republic. rene.labounek@gmail.com. Department of Neurology, Palacký University, Olomouc, Czech Republic. rene.labounek@gmail.com.
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- $a Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.
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