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01-01-2018 | Original Paper

A Tutorial Review on Multi-subject Decomposition of EEG

Authors: René J. Huster, Liisa Raud

Published in: Brain Topography | Issue 1/2018

Abstract

Over the last years we saw a steady increase in the relevance of big neuroscience data sets, and with it grew the need for analysis tools capable of handling such large data sets while simultaneously extracting properties of brain activity that generalize across subjects. For functional magnetic resonance imaging, multi-subject or group-level independent component analysis provided a data-driven approach to extract intrinsic functional networks, such as the default mode network. Meanwhile, this methodological framework has been adapted for the analysis of electroencephalography (EEG) data. Here, we provide an overview of the currently available approaches for multi-subject data decomposition as applied to EEG, and highlight the characteristics of EEG that warrant special consideration. We further illustrate the importance of matching one’s choice of method to the data characteristics at hand by guiding the reader through a set of simulations. In sum, algorithms for group-level decomposition of EEG provide an innovative and powerful tool to study the richness of functional brain networks in multi-subject EEG data sets.

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Title: A Tutorial Review on Multi-subject Decomposition of EEG
Authors: René J. Huster
Liisa Raud
Publication date: 01-01-2018
Publisher: Springer US
Published in: Brain Topography / Issue 1/2018
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-017-0603-x

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

A Tutorial Review on Multi-subject Decomposition of EEG

Abstract

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

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