Data and code from: Preserved temporal hierarchy but frequency-specific alterations in dynamical regimes of EEG microstate multimers during reversible unconsciousness

Employing a spectral analysis framework based on Chaos Game Representation
(CGR), we investigated the higher-order dynamics of EEG microstate
sequences across delta, theta, alpha, beta, gamma, and broad frequency
bands during reversible unconsciousness (anesthesia and sleep). Robust
periodic components consistently emerged within microstate sequences
across theta, alpha, beta, and gamma bands, persisting across distinct
states of consciousness. Converging evidence from both deconstruction via
surrogate data and reconstruction via a hierarchical generative model
demonstrates that the multimer structure, along with the conditional
duration distribution, constitutes the underlying mechanism of microstate
periodicity. Furthermore, we show that temporal smoothing abolishes these
intrinsic periodic components. Most notably, during both deep sedation and
N3 sleep, the beta band microstate sequence exhibited a consistent
increase in peak power and a decrease in center frequency, resulting in
highly characteristic patterns in the CGR spectra. To dissect the
structural basis of these periodicities, we developed a data-driven
algorithm to extract multimers and calculate their metrics. We identified
distinct, frequency-dependent alterations in multimer dynamics during
reversible unconsciousness, suggesting that the transition to
unconsciousness marks a shift towards specific dynamical regimes.
Collectively, our findings confirm that microstate sequences exhibit
precise temporal orchestration. By elucidating the generative mechanisms
of microstate periodicity and establishing a multimer-based analytical
framework, this study provides a solid methodological foundation for
investigating higher-order temporal structures, while offering promising
neurophysiological biomarkers for consciousness assessment and novel
insights into the temporal organization of large-scale neural dynamics.