Gesine Hermann

This paper examines how temporal smoothing and interpolation affect EEG microstate parameters, showing that strong smoothing alters brain dynamics and recommends avoiding intense smoothing to preserve accurate representations.

This paper challenges the use of the meta-criterion for determining cluster numbers in microstate analysis, showing it can suggest spurious optimal cluster numbers and that EEG topographies form a single connected structure rather than clusters.

The paper compares entropy rate and sample entropy metrics for analyzing higher-order EEG microstate syntax in Alzheimer's disease and control groups, finding differences that disappear when normalized to first-order syntax.

The paper evaluates surrogate data generation methods for EEG microstate syntax analysis, finding that Markov chain surrogates are valid while shuffling is not.