ArticleJournal of neuroengineering and rehabilitation2026
Microstate permutation complexity of EEG signals distinguishes minimally conscious state plus from minimally conscious state minus.
Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAccurately distinguishing minimally conscious state plus (MCS+) from minimally conscious state minus (MCS-) is critical for prognosis and treatment planning. Microstate analysis decomposes multichannel electroencephalography (EEG) into a sequence of brief, relatively stable scalp electric-field topographies, offering a unique spatiotemporal perspective on brain activity. Yet applications of microstate methods to the assessment of disorders of consciousness remain scarce. Moreover, most state-of-the-art studies focus on characterizing the complexity of microstate sequences, while conventional complexity measures overlook transitions between microstates. To address this gap, we propose Microstate Permutation Lempel-Ziv Complexity (MS-PLZC), an extension of Lempel-Ziv complexity that explicitly encodes ordinal permutation information to more sensitively capture the temporal organization of microstate sequences.
methodsResting-state EEG was recorded from 45 individuals with disorders of consciousness (15 unresponsive wakefulness syndrome, 15 MCS-, 15 MCS+) and 15 neurologically healthy controls. MS-PLZC, conventional microstate LZC, spectral power, sample entropy, and classical LZC were calculated and statistically compared. These features were assessed using a nested leave-one-out cross-validated (LOOCV) SVM with exhaustive hyper-parameter search.
resultsBoth MS-LZC and MS-PLZC showed statistically significant group differences (Kruskal-Wallis test: MS-LZC: H = 26.92, p < 0.0000, η²=0.2099; MS-PLZC: H = 35.11, p < 0.0000, η²=0.2816), with MS-PLZC exhibiting greater statistical power. Notably, MS-PLZC successfully distinguished between MCS- and MCS+ patients (p _adj < 0.05) with a large effect size (Cliff's Delta = -0.6178), whereas MS-LZC demonstrated only a medium effect size (Cliff's Delta = -0.3067). In the machine-learning analysis MS-PLZC achieved the highest leave-one-out accuracy (0.733) and ROC-AUC (0.733).
conclusionsThese results indicate that MS-PLZC sensitively captures subtle shifts in microstate dynamics and offers a reliable single-feature discriminator of MCS+ versus MCS-, with translational potential for detecting key recovery windows during routine assessment of consciousness.
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