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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 …