EEG microstates provides a unique perspective on the dynamic functional organization of the brain, has attracted considerable attention. This study conducted a bibliometric analysis of 441 articles retrieved from the Web of Science database using CiteSpace. The analysis focused on four key areas of EEG microstates: The academic network, classification and function, reliability of parameter, and primary research topics. Results revealed a steady increase in annual publications within this field. The academic network was dominated by the countries (including Switzerland, the United States, and China), the journal NeuroImage, and prominent researchers such as Koenig, Michel, and Lehmann. Determining the optimal number and the functions of EEG microstate classes were attributed to the clustering algorithms and validation criteria. The reliability of EEG microstate parameters varied, with mean duration demonstrating high reliability while the lower reliability was observed for transition probabilities (TP). Primary research topics encompassed cognitive function exploration, developmental changes, psychiatric disorder diagnosis, and emotion recognition. Future research should prioritize developing standardized labeling criteria for EEG microstate classes. Additionally, caution should be exercised when using TP. Moreover, integrating EEG microstate analysis with machine learning techniques provides significant advantages for the advancement and execution of related research endeavors.