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Brain Topogr. 2018 Jan;31(1):76-89. doi: 10.1007/s10548-017-0585-8. Epub 2017 Sep 5.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

Author information

1
Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic. rene.labounek@gmail.com.
2
Central European Institute of Technology, Masaryk University, Brno, Czech Republic. rene.labounek@gmail.com.
3
Department of Neurology, Palacký University, Olomouc, Czech Republic. rene.labounek@gmail.com.
4
Mind Research Network, Albuquerque, NM, 87106, USA.
5
Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
6
Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic.
7
Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
8
Division of Endocrinology and Diabetes, University of Minnesota, Minneapolis, MN, USA.
9
Department of Mathematics, Brno University of Technology, Brno, Czech Republic.
10
Department of Neurology, Palacký University, Olomouc, Czech Republic.
11
Department of Neurology, University Hospital Olomouc, Olomouc, Czech Republic.

Abstract

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.

KEYWORDS:

EEG; ICA; Multi-subject blind source separation; Resting-state; Semantic decision; Spatiospectral patterns; Visual oddball

PMID:
28875402
DOI:
10.1007/s10548-017-0585-8
[Indexed for MEDLINE]

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