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


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.


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


  1. Antonakakis M, Zervakis M, van Beijsterveldt CEM et al (2016) Genetic effects on source level evoked and induced oscillatory brain responses in a visual oddball task. Biol Psychol 114:69–80. doi: 10.1016/j.biopsycho.2015.12.006 CrossRefPubMedGoogle Scholar
  2. Bigdely-Shamlo N, Mullen T, Kreutz-Delgado K, Makeig S (2013) Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference. Neuroimage 72:287–303. doi: 10.1016/j.neuroimage.2013.01.040
  3. Bridwell DA, Wu L, Eichele T, Calhoun VD (2013) The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps. Neuroimage 69:101–111. doi: 10.1016/j.neuroimage.2012.12.024 CrossRefPubMedGoogle Scholar
  4. Bridwell DA, Kiehl KA, Pearlson GD, Calhoun VD (2014) Patients with schizophrenia demonstrate reduced cortical sensitivity to auditory oddball regularities. Schizophr Res 158:189–194. doi: 10.1016/j.schres.2014.06.037 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bridwell DA, Steele VR, Maurer JM et al (2015) The relationship between somatic and cognitive-affective depression symptoms and error-related ERPs. J Affect Disord 172:89–95. doi: 10.1016/j.jad.2014.09.054 CrossRefPubMedGoogle Scholar
  6. Bridwell DA, Rachakonda S, Silva RF et al (2016) Spatiospectral decomposition of multi-subject EEG: evaluating blind source separation algorithms on real and realistic simulated data. Brain Topogr. doi: 10.1007/s10548-016-0479-1 PubMedPubMedCentralGoogle Scholar
  7. Calhoun VD, Adali T (2012) Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng 5:60–73. doi: 10.1109/RBME.2012.2211076 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Carroll JD, Chang J-J (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition. Psychometrika 35:283–319. doi: 10.1007/BF02310791 CrossRefGoogle Scholar
  9. Cong F, He Z, Hämäläinen J et al (2013) Validating rationale of group-level component analysis based on estimating number of sources in EEG through model order selection. J Neurosci Methods 212:165–172. doi: 10.1016/j.jneumeth.2012.09.029 CrossRefPubMedGoogle Scholar
  10. Congedo M, John RE, De Ridder D, Prichep L (2010) Group independent component analysis of resting state EEG in large normative samples. Int J Psychophysiol 78:89–99. doi: 10.1016/j.ijpsycho.2010.06.003 CrossRefPubMedGoogle Scholar
  11. Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21. doi: 10.1016/j.jneumeth.2003.10.009 CrossRefPubMedGoogle Scholar
  12. Delorme A, Palmer J, Onton J et al (2012) Independent EEG sources are dipolar. PLoS ONE 7:e30135. doi: 10.1371/journal.pone.0030135 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Eichele T, Rachakonda S, Brakedal B et al (2011) EEGIFT: group independent component analysis for event-related EEG data. Comput Intell Neurosci. doi: 10.1155/2011/129365 PubMedPubMedCentralGoogle Scholar
  14. Enriquez-Geppert S, Barceló F (2016) Multisubject decomposition of event-related positivities in cognitive control: tackling age-related changes in reactive control. Brain Topogr. doi: 10.1007/s10548-016-0512-4 PubMedGoogle Scholar
  15. Himberg J, Hyvärinen A, Esposito F (2004) Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage 22(3):1214–1222CrossRefPubMedGoogle Scholar
  16. Huster RJ, Plis SM, Lavallee CF et al (2014) Functional and effective connectivity of stopping. Neuroimage 94:120–128. doi: 10.1016/j.neuroimage.2014.02.034 CrossRefPubMedGoogle Scholar
  17. Huster RJ, Plis SM, Calhoun VD (2015) Group-level component analyses of EEG: validation and evaluation. Front Neurosci 9:254. doi: 10.3389/fnins.2015.00254 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Huster RJ, Schneider S, Lavallee CF et al (2017) Filling the void-enriching the feature space of successful stopping. Hum Brain Mapp 38:1333–1346. doi: 10.1002/hbm.23457 CrossRefPubMedGoogle Scholar
  19. Kovacevic N, McIntosh AR (2007) Groupwise independent component decomposition of EEG data and partial least square analysis. Neuroimage 35:1103–1112. doi: 10.1016/j.neuroimage.2007.01.016 CrossRefPubMedGoogle Scholar
  20. Lio G, Boulinguez P (2016) How does sensor-space group blind source separation face inter-individual neuroanatomical variability? Insights from a simulation study based on the PALS-B12 atlas. Brain Topogr. doi: 10.1007/s10548-016-0497-z PubMedGoogle Scholar
  21. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci (Regul Ed) 8:204–210. doi: 10.1016/j.tics.2004.03.008 CrossRefGoogle Scholar
  22. Michel CM, Murray MM, Lantz G et al (2004) EEG source imaging. Clin Neurophysiol 115:2195–2222. doi: 10.1016/j.clinph.2004.06.001 CrossRefPubMedGoogle Scholar
  23. Mørup M, Hansen LK, Arnfred SM (2007) ERPWAVELAB a toolbox for multi-channel analysis of time-frequency transformed event related potentials. J Neurosci Methods 161:361–368. doi: 10.1016/j.jneumeth.2006.11.008 CrossRefPubMedGoogle Scholar
  24. Onton J, Delorme A, Makeig S (2005) Frontal midline EEG dynamics during working memory. Neuroimage 27:341–356. doi: 10.1016/j.neuroimage.2005.04.014 CrossRefPubMedGoogle Scholar
  25. Onton J, Westerfield M, Townsend J, Makeig S (2006) Imaging human EEG dynamics using independent component analysis. Neurosci Biobehav Rev 30:808–822. doi: 10.1016/j.neubiorev.2006.06.007 CrossRefPubMedGoogle Scholar
  26. Ramkumar P, Parkkonen L, Hyvärinen A (2014) Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. Neuroimage 86:480–491. doi: 10.1016/j.neuroimage.2013.10.032 CrossRefPubMedGoogle Scholar
  27. Rashid B, Arbabshirani MR, Damaraju E et al (2016) Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage 134:645–657. doi: 10.1016/j.neuroimage.2016.04.051 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Skrandies W (1993) EEG/EP: new techniques. Brain Topogr 5:347–350CrossRefPubMedGoogle Scholar
  29. Stoica P, Babu P (2012) On the proper forms of BIC for model order selection. IEEE Trans Signal Process 60:4956–4961CrossRefGoogle Scholar
  30. Tichavský P, Koldovský Z, Yeredor A et al (2008) A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach. IEEE Trans Neural Netw 19:421–430. doi: 10.1109/TNN.2007.908648 CrossRefPubMedGoogle Scholar
  31. Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31:279–311. doi: 10.1007/BF02289464 CrossRefPubMedGoogle Scholar
  32. van Dinteren R, Huster RJ, Jongsma MLA et al (2017) Differences in cortical sources of the event-related P3 potential between young and old participants indicate frontal compensation. Brain Topogr. doi: 10.1007/s10548-016-0542-y PubMedGoogle Scholar
  33. Viola FC, Thorne J, Edmonds B et al (2009) Semi-automatic identification of independent components representing EEG artifact. Clin Neurophysiol 120:868–877. doi: 10.1016/j.clinph.2009.01.015 CrossRefPubMedGoogle Scholar
  34. Wessel JR, Ullsperger M (2011) Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity. Neuroimage 54:2105–2115. doi: 10.1016/j.neuroimage.2010.10.033 CrossRefPubMedGoogle Scholar
  35. Williams DB (1994) Counting the degrees of freedom when using AIC and MDL to detect signals. IEEE Trans Signal Process 42:3282–3284CrossRefGoogle Scholar
  36. Yuan H, Zotev V, Phillips R et al (2012) Spatiotemporal dynamics of the brain at rest–exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. Neuroimage 60:2062–2072. doi: 10.1016/j.neuroimage.2012.02.031 CrossRefPubMedGoogle Scholar

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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