The microstate analysis, which is based on the clustering of electric field maps, has been proved to be an efficient technique that could fully use the spatial information of topographies of EEG/ERP signals. Typically, researchers found that only four kinds of distinct topographies (i.e., microstate classes) could explain most variances of the spontaneous EEG data. Moreover, each of the four commonly observed microstate classes was closely associated with the activity of distinct resting-state brain networks revealed by BOLD signals of resting-state fMRI. For ERP signals, the microstate segmentation can be used to identify the underlying ERP components and their latencies from the multichannel ERP waveforms. In this chapter, we illustrated the basic concepts of microstate analysis, the commonly used clustering algorithms, the metrics derived from microstate analysis, and how to perform microstate analysis using open-access tools.
KeywordsTopographic maps Spontaneous EEG ERP Spatial clustering
- Brunet D, Murray MM, Michel CM. Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. 2010;2011:813870.Google Scholar
- Rieger K, Diaz Hernandez L, Baenninger A, Koenig T. 15 years of microstate research in schizophrenia – where are we? A meta-analysis. Front Psych. 2016;7:22.Google Scholar