Short-term and long-term test-retest reliability of memory, complexity, and randomness of EEG microstates sequence

EEG microstates sequence analysis gained a lot of attention in recent years and different sequence analysis methods have been applied to study microstates sequence randomness, complexity, speed, periodicity, and long-range memory. Although several studies have reported on the reliability of temporal parameters, the stability of sequence-based metrics within subjects has not yet been systematically examined. In this study, we analysed EEG recordings from 60 healthy young adults and assessed short-term (90 min) and long-term (30 days) test–retest reliability and agreement of sequence measures: long-range memory (Hurst exponent), complexity (two Lempel–Ziv algorithms), and randomness (entropy and entropy rate). Across metrics, short-term reliability was consistently good to excellent (ICC = 0.831–0.902), and long-term reliability was moderate to good (ICC = 0.651–0.793). Entropy and entropy rate emerged as the most stable measures across both intervals, confirmed by minimal bias and strong agreement. These findings demonstrate that EEG microstate sequence dynamics represent a stable trait of neural activity, providing a solid methodological foundation for future studies that aim to embed these metrics into computational models and explore their translational value as neurophysiological biomarkers.