EEG Electric Field Topography is Stable During Moments of High Field Strength


Spontaneous broadband electroencephalography (EEG) demonstrates short moments of stability in the spatial distribution of the head-surface voltage topography. This phenomenon underlies the premise behind segmenting multichannel EEG into topographically defined brain states, known as EEG microstates. Microstate segmentation methods commonly identify representative topographical configurations based on clustering applied to a subset of voltage maps selected at the time series points of greatest strength in the neuroelectric field. These moments are well-reasoned to best represent periods of momentary stability in the voltage topography, and consequently, points of greatest signal relative to noise. Yet, more direct empirical evidence for these assumptions is warranted, and the consistency of this phenomenon across individuals has not been characterized. In the present investigation, the association between electric field strength and topographic dissimilarity of temporally adjacent samples of EEG were characterized in a large sample of healthy adults engaged in quiet rest. Samples of individuals’ EEG time series high in electric field strength were found to be topographically similar relative to adjacent time series samples. The strong phase-synchronized actvity of neuronal populations therefore coincides with momentary stability in the topographic voltage configuration, providing robust empirical support for the basic premise underlying segmentation of broadband EEG into microstates.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    The mean correlations were nearly identical when including the 12 individuals originally excluded from the sample. Correlations for all 203 individuals were large on average in both the eyes closed (mean r = − 0.657, SD = 0.060, 95% CI [− 0.666, − 0.649]) and eyes open conditions (mean r = − 0.663, SD = 0.064, 95% CI [− 0.672, − 0.654]).


  1. Babayan A, Erbey M, Kumral D, Reinelt JD, Reiter AMF, Röbbig J, Villringer A (2019) A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data 6:180308

    Article  Google Scholar 

  2. Brechet L, Brunet D, Birot G, Gruetter R, Michel CM, Jorge J (2019) Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. Neuroimage 194:82–92

    Article  Google Scholar 

  3. Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170

    Article  Google Scholar 

  4. Brunet D, Murray MM, Michel CM (2011) Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci 2(1–2):15

    Google Scholar 

  5. Custo A, Van De Ville D, Wells WM, Tomescu MI, Brunet D, Michel CM (2017) Electroencephalographic resting-state networks: source localization of microstates. Brain Connect 7(10):671–682

    Article  Google Scholar 

  6. Dinov M, Leech R (2017) Modeling uncertainties in EEG microstates: analysis of real and imagined motor movements using probabilistic clustering-driven training of probabilistic neural networks. Front Hum Neurosci 11:534.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Khanna A, Pascual-Leone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113

    Article  Google Scholar 

  8. Koenig T, Brandeis D (2016) Inappropriate assumptions about EEG state changes and their impact on the quantification of EEG state dynamics. Neuroimage 125:1104–1106

    Article  Google Scholar 

  9. Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288

    CAS  Article  Google Scholar 

  10. Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage 180:577–593

    Article  Google Scholar 

  11. Michel CM, Koenig T, Brandeis D (2009) Electric neuroimaging in the time domain. In: Michel CM et al (eds) Electrical neuroimaging. Cambridge University Press, Cambridge, pp 111–144

    Google Scholar 

  12. Mishra A, Englitz B, Cohen MX (2020) EEG microstates as a continuous phenomenon. NeuroImage 208:116454.

    Article  PubMed  Google Scholar 

  13. Murray MM, Brunet D, Michel CM (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 4:249–264

    Article  Google Scholar 

  14. Seitzman BA, Abell M, Bartley SC, Erickson MA, Bolbecker AR, Hetrick WP (2017) Cognitive manipulation of brain electric microstates. Neuroimage 146:533–543

    Article  Google Scholar 

  15. Skrandies W (1990) Global field power and topographic similarity. Brain Topogr 3(1):137–141

    CAS  Article  Google Scholar 

  16. Vaughan HG (1982) The neural origins of human event-related potentials. Ann N Y Acad Sci 388(1):125–138

    Article  Google Scholar 

  17. Wackermann J, Lehmann D, Michel CM, Strik WK (1993) Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 14:269–283

    CAS  Article  Google Scholar 

  18. Wittchen H-U, Zaudig M, Fydrich T (1997) SKID. Strukturiertes klinisches interview für DSM-IV. Achse I und II. Handanweisung. Hogrefe, Göttingen

    Google Scholar 

  19. Zanesco AP, King BG, Skwara AC, Saron CD (2020) Within and between-person correlates of the temporal dynamics of resting EEG microstates. NeuroImage 211:116631.

    Article  PubMed  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Anthony P. Zanesco.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

I thank the Mind-Body-Emotion group at the Max Planck Institute for Human Cognitive and Brain Sciences who gathered and made available the de-identified data on which this manuscript is based. I utilized the freely available Cartool software toolbox ( programmed by Denis Brunet, from the Functional Brain Mapping Laboratory, Geneva, Switzerland, and supported by the Center for Biomedical Imaging of Geneva and Lausanne. The data supporting the findings of this study are openly available in the OSF repository and can be found at:

Handling Editor: Christoph M. Michel.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zanesco, A.P. EEG Electric Field Topography is Stable During Moments of High Field Strength. Brain Topogr 33, 450–460 (2020).

Download citation


  • EEG
  • Global field power
  • Microstates
  • Topographic dissimilarity