Publication detail

Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input

LABOUNEK, R. BRIDWELL, D. MAREČEK, R. LAMOŠ, M. MIKL, M. BRÁZDIL, M. JAN, J. HLUŠTÍK, P.

Original Title

Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input

English Title

Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input

Type

conference paper

Language

en

Original Abstract

Within the last decade, various blind source separation algorithms (BSS) isolating distinct EEG oscillations were derived and implemented. Group Independent Component Analysis (group-ICA) is a promising tool for decomposing spatiospectral EEG maps across multiple subjects. However, researchers are faced with many preprocessing options prior to performing group-ICA, which potentially influences the results. To examine the influence of preprocessing steps, within this article we compare results derived from group-ICA using the absolute power of spatiospectral maps and the relative power of spatiospectral maps. Within a previous study, we used K-means clustering to demonstrate group-ICA of absolute power spatiospectral maps generates sources which are stable across different paradigms (i.e. resting-state, semantic decision, visual oddball) Within the current study, we compare these maps with those obtained using relative power of spatiospectral maps as input to group-ICA. We find that relative EEG power contains 10 stable spatiospectral patterns which were similar to those observed using absolute power as inputs. Interestingly, relative power revealed two c-band (20–40 Hz) patterns which were present across 3 paradigms, but not present using absolute power. This finding suggests that relative power potentially emphasizes low energy signals which are obscured by the high energy low frequency which dominates absolute power measures.

English abstract

Within the last decade, various blind source separation algorithms (BSS) isolating distinct EEG oscillations were derived and implemented. Group Independent Component Analysis (group-ICA) is a promising tool for decomposing spatiospectral EEG maps across multiple subjects. However, researchers are faced with many preprocessing options prior to performing group-ICA, which potentially influences the results. To examine the influence of preprocessing steps, within this article we compare results derived from group-ICA using the absolute power of spatiospectral maps and the relative power of spatiospectral maps. Within a previous study, we used K-means clustering to demonstrate group-ICA of absolute power spatiospectral maps generates sources which are stable across different paradigms (i.e. resting-state, semantic decision, visual oddball) Within the current study, we compare these maps with those obtained using relative power of spatiospectral maps as input to group-ICA. We find that relative EEG power contains 10 stable spatiospectral patterns which were similar to those observed using absolute power as inputs. Interestingly, relative power revealed two c-band (20–40 Hz) patterns which were present across 3 paradigms, but not present using absolute power. This finding suggests that relative power potentially emphasizes low energy signals which are obscured by the high energy low frequency which dominates absolute power measures.

Keywords

EEG, Spatiospectral ICA, Multisubject blind source separation

Released

01.05.2019

Publisher

Springer, Singapore

ISBN

978-981-10-9037-0

Book

World Congress on Medical Physics and Biomedical Engineering 2018

Pages from

125

Pages to

128

Pages count

4

URL

BibTex


@inproceedings{BUT150086,
  author="René {Labounek} and David {Bridwell} and Radek {Mareček} and Martin {Lamoš} and Michal {Mikl} and Milan {Brázdil} and Jiří {Jan} and Petr {Hluštík}",
  title="Stable EEG Spatiospectral Sources Using Relative Power as Group-ICA Input",
  annote="Within the last decade, various blind source separation algorithms (BSS) isolating distinct EEG oscillations were derived and implemented. Group Independent Component Analysis (group-ICA) is a promising tool for decomposing spatiospectral EEG maps across multiple subjects. However, researchers are faced with many preprocessing options prior to performing group-ICA, which potentially influences the results. To examine the influence of preprocessing steps, within this article we compare results derived from group-ICA using the absolute power of spatiospectral maps and the relative power of spatiospectral maps. Within a previous study, we used K-means clustering to demonstrate group-ICA of absolute power spatiospectral maps generates sources which are stable across different paradigms (i.e. resting-state, semantic decision, visual oddball) Within the current study, we compare these maps with those obtained using relative power of spatiospectral maps as input to group-ICA. We find that relative EEG power contains 10 stable spatiospectral patterns which were similar to those observed using absolute power as inputs. Interestingly, relative power revealed two c-band (20–40 Hz) patterns which were present across 3 paradigms, but not present using absolute power. This finding suggests that relative power potentially emphasizes low energy signals which are obscured by the high energy low frequency which dominates absolute power measures.",
  address="Springer, Singapore",
  booktitle="World Congress on Medical Physics and Biomedical Engineering 2018",
  chapter="150086",
  doi="10.1007/978-981-10-9038-7_22",
  howpublished="print",
  institution="Springer, Singapore",
  number="2",
  year="2019",
  month="may",
  pages="125--128",
  publisher="Springer, Singapore",
  type="conference paper"
}