Electroencephalography (EEG) has been widely used in human brain research. Several techniques in EEG relies on analyzing the topographical distribution of the data. One of the most common analysis is EEG microstate (EEG-ms). EEG-ms reflects the stable topographical representation of EEG signal lasting a few dozen milliseconds. EEG-ms were associated with resting state fMRI networks and were associated with mental processes and abnormalities. One challenge in EEG-ms analysis is the polarity invariant property for the signal, in which the relative direction of local minima and maxima is taking into consideration. Thus, identifying those topographies requires special handling for the data using modified clustering algorithms. Here, we propose a polarity invariant transformation for EEG data to eliminate the difficulties with handling the polarity of the data during the EEG-ms identification part, which would allow better clustering EEG data. Our results demonstrate how the transformation work and show the benefit of using such transformation.