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Brain Topography

, Volume 31, Issue 1, pp 3–16 | Cite as

A Tutorial Review on Multi-subject Decomposition of EEG

  • René J. Huster
  • Liisa Raud
Original Paper

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

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

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Multimodal Imaging and Cognitive Control Lab, Department of PsychologyUniversity of OsloOsloNorway
  2. 2.Psychology Clinical Neurosciences CenterUniversity of New MexicoAlbuquerqueUSA
  3. 3.Cognitive Electrophysiology Cluster, Department of PsychologyUniversity of OsloOsloNorway

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