Publication detail

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

LABOUNEK, R. BRIDWELL, D. MAREČEK, R. LAMOŠ, M. MIKL, M. SLAVÍČEK, T. BEDNAŘÍK, P. BAŠTINEC, J. HLUŠTÍK, P. BRÁZDIL, M. JAN, J.

Original Title

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

English Title

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Type

journal article

Language

en

Original 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.

English 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, Spatiospectral patterns, Multisubject blind source separation, Resting-state, Semantic decision, Visual oddball

Released

17.01.2018

Publisher

Springer

Pages from

76

Pages to

89

Pages count

14

URL

BibTex


@article{BUT139163,
  author="René {Labounek} and David {Bridwell} and Radek {Mareček} and Martin {Lamoš} and Michal {Mikl} and Tomáš {Slavíček} and Petr {Bednařík} and Jaromír {Baštinec} and Petr {Hluštík} and Milan {Brázdil} and Jiří {Jan}",
  title="Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA",
  annote="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.",
  address="Springer",
  chapter="139163",
  doi="10.1007/s10548-017-0585-8",
  howpublished="print",
  institution="Springer",
  number="1",
  volume="31",
  year="2018",
  month="january",
  pages="76--89",
  publisher="Springer",
  type="journal article"
}