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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,
Language English Country United States
Document type Journal Article, Research Support, Non-U.S. Gov't
NLK
ProQuest Central
from 1999-07-01 to 1 year ago
Medline Complete (EBSCOhost)
from 2009-05-01 to 1 year ago
Health & Medicine (ProQuest)
from 1999-07-01 to 1 year ago
Psychology Database (ProQuest)
from 1999-07-01 to 1 year ago
- MeSH
- Algorithms MeSH
- Principal Component Analysis MeSH
- Electroencephalography methods statistics & numerical data MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain Mapping methods MeSH
- Young Adult MeSH
- Signal Processing, Computer-Assisted MeSH
- Psychomotor Performance physiology MeSH
- Reproducibility of Results MeSH
- Decision Making physiology MeSH
- Cluster Analysis MeSH
- Visual Perception physiology MeSH
- Check Tag
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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
References provided by 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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