Quantum Error Correction with Imperfect Gates

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. 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. The online version contains supplementary material available at 10.1007/s11571-025-10391-2.

Keywords

Affiliated Institutions

• Landau Institute for Theoretical Physics RU

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