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

Abstract 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. A few reliability studies reported somewhat consistent results of temporal parameters, yet no study so far addressed the within subject stability and reliability over time of different microstate sequence metrics. Here, we performed EEG microstate segmentation on data recorded from 60 healthy young adults and evaluated short-term (90 min), and long-term (30 days) reliability and agreement of EEG microstate sequence long-range memory as estimated with Hurst exponent, complexity as evaluated with two different Lempel-Ziv complexity algorithms, and its randomness as quantified with entropy and entropy rate. Our results showed mostly good short-term reliability across all 5 metrics (0.831 < ICC < 0.902), and moderate to good (0.651 < ICC < 0.793) long-term reliability. Reliability and agreement over time demonstrated in this work strongly suggests that microstates sequence dynamics is a stable trait of neural activity that can be utilised as a possible reliable neurophysiological biomarker.