  Functional brain networks exhibit dynamics on the sub-second temporal scale
and are often assumed to embody the physiological substrate of cognitive
processes. Here we analyse the temporal and spatial dynamics of these states,
as measured by EEG, with a hidden Markov model and compare this approach to
classical EEG microstate analysis. We find dominating state lifetimes of
100--150\,ms for both approaches. The state topographies show obvious
similarities. However, they also feature distinct spatial and especially
temporal properties. These differences may carry physiological meaningful
information originating from patterns in the data that the HMM is able to
integrate while the microstate analysis is not. This hypothesis is supported by
a consistently high pairwise correlation of the temporal evolution of EEG
microstates which is not observed for the HMM states and which seems unlikely
to be a good description of the underlying physiology. However, further
investigation is required to determine the robustness and the functional and
clinical relevance of EEG HMM states in comparison to EEG microstates.
