EEG microstates: from methodological foundations to clinical translation

Electroencephalography (EEG) microstates represent discrete topographic configurations persisting approximately 50-120 ms before transitioning to new patterns. These transient states provide unique insights into large-scale brain organization in health and disease, capturing reference-independent, zero-lag, synchronized networks at timescales matching cortical communication constraints. This review addresses critical methodological challenges in EEG microstates, including data-driven cluster optimization, template derivation strategies for group comparisons, and interpretive frameworks based on convergent evidence rather than premature functional consensus. Future advances will require transitioning from arbitrary conventions to optimization-based approaches, characterizing higher-order temporal dynamics, establishing multimodal integration, and building validation standards that prioritize convergent evidence. With methodological rigor, microstate analysis can advance our understanding of brain dynamics and their role in cognition, consciousness, and brain disorders.