Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

René Labounek, David A. Bridwell, Radek Mareček, Martin Lamoš, Michal Mikl, Tomáš Slavíček, Petr Bednařík, Jaromír Baštinec, Petr Hluštík, Milan Brázdil, Jiří Jan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)76-89
Number of pages14
JournalBrain Topography
Volume31
Issue number1
DOIs
StatePublished - Jan 1 2018

Bibliographical note

Funding Information:
We would like to thank Dr. Milena Kolov? for her help with designing the semantic decision task. This research was supported Grant No. P304/11/1318 of Grant Agency of Czech Republic, by Grants Nos. FEKT-S-14-2210 and FEKT-S-11-2-921 of Brno University of Technology, by Grants Nos. CZ.1.05/1.1.00/02.0068 of Central European Institute of Technology and by Grants Nos. AZV 16-302100A of Palack? University. The funding is highly acknowledged. Computational resources were provided by the MetaCentrum under the program LM2010005 and the CERIT-SC under the program Centre CERIT Scientific Cloud, part of the Operational Program Research and Development for Innovations, Reg. No. CZ.1.05/3.2.00/08.0144. This is one of several papers published together in Brain Topography on the ?Special Issue: Multisubject decomposition of EEG - methods and applications?.

Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.

Keywords

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

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