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Brain Topogr. 2018 Jan;31(1):3-16. doi: 10.1007/s10548-017-0603-x. Epub 2017 Oct 23.

A Tutorial Review on Multi-subject Decomposition of EEG.

Huster RJ1,2,3, Raud L4,5.

Author information

1
Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway. rene.huster@psykologi.uio.no.
2
Psychology Clinical Neurosciences Center, University of New Mexico, Albuquerque, USA. rene.huster@psykologi.uio.no.
3
Cognitive Electrophysiology Cluster, Department of Psychology, University of Oslo, Oslo, Norway. rene.huster@psykologi.uio.no.
4
Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway.
5
Cognitive Electrophysiology Cluster, Department of Psychology, University of Oslo, Oslo, Norway.

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.

KEYWORDS:

Blind source separation; Decomposition; EEG; Group ICA; Group-level; Multi-subject

PMID:
29063237
DOI:
10.1007/s10548-017-0603-x
[Indexed for MEDLINE]

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