EEG microstates as a continuous phenomenon

Neuroimage. 2020 Mar:208:116454. doi: 10.1016/j.neuroimage.2019.116454. Epub 2019 Dec 10.


In recent years, EEG microstate analysis has gained popularity as a tool to characterize spatio-temporal dynamics of large-scale electrophysiology data. It has been used in a wide range of EEG studies and the discovered microstates have been linked to cognitive function and brain diseases. EEG microstates are assumed to (1) be winner-take-all, meaning that the topography at any given time point is in one state; and (2) discretely transition from one state into another. In this study we investigated these assumptions by taking a geometric perspective of EEG data, treating microstate topographies as basis vectors for a subspace of the original channel space. We found that within- and across-microstate distance distributions were largely overlapping: for the low GFP (Global Field Power) range (lower 15%), individual time points labeled as one microstate are often equidistant to multiple microstate vectors, challenging the winner-take-all assumption. At high global field power, separability of microstates improved, but remained rather weak. Although many GFP peaks (which are the time points used for defining microstates) occur during high GFP ranges, low GFP ranges associated with poor separability also contain GFP peaks. Furthermore, the geometric analysis suggested that microstates and their transitions appear to be more continuous than discrete. The Analysis of rate of change of trajectory in sensor space suggests gradual microstate transitions as opposed to the classical binary view of EEG microstates. Taken together, our findings suggest that EEG microstates are better conceptualized as spatially and temporally continuous, rather than discrete activations of neural populations.

Keywords: Cortical dynamics; EEG microstates; Electroencephalography; k-means.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cerebral Cortex / physiology*
  • Computer Simulation
  • Datasets as Topic
  • Electroencephalography / methods*
  • Functional Neuroimaging / methods*
  • Humans
  • Models, Statistical
  • Signal Processing, Computer-Assisted*