Schematic of the method of microstate analysis and extraction of features of interest from the microstate time series.

<p>(<b>A</b>) The GFP (drawn in red) is calculated at each instant of the multichannel EEG recording. Peaks of the GFP curve represent moments of highest SNR. At peaks of the GFP curve, the potential recorded at each electrode of the multichannel signal is plotted onto a map of the channel array. This collection of maps is entered into a clustering algorithm (TAAHC or <i>k</i>-means clustering), which results in a small number of representative microstate maps that explain a large proportion of the global topographic variance. Four topographies are repeatedly found using this method; these maps are labeled A, B, C, or D in the figure. Crosshairs indicate points of maximum or minimum recorded electric potential. (<b>B</b>) The original maps at peaks of the GFP curve are assigned to a microstate class A, B, C, or D based on the degree of correlation with the microstate maps and statistical smoothing of the time series. This reassignment results in a representation of the original multichannel data as an alternating series of microstates A, B, C, and D. A microstate is considered dominant in the time during which all successive original maps are assigned to the same microstate class, starting and ending at the midpoint in time between the last original map of the preceding microstate and the first original map of the following microstate, respectively. Each period of dominance is considered a unique appearance of a microstate. The frequency of a microstate is the number of unique appearances per second. The coverage of each microstate is the fraction of total recording time that each microstate is dominant.