What question did this study set out to answer?

The aim is to distinguish between minimally conscious state plus (MCS+) and minus (MCS-) using EEG microstate analysis.

May 3, 2026

Microstate permutation complexity of EEG signals distinguishes minimally conscious state plus from minimally conscious state minus.

Puntos clave

The aim is to distinguish between minimally conscious state plus (MCS+) and minus (MCS-) using EEG microstate analysis.
Resting-state EEG recorded from 45 individuals with disorders of consciousness and 15 healthy controls.
Microstate Permutation Lempel-Ziv Complexity (MS-PLZC) was calculated and compared with conventional measures using a nested leave-one-out cross-validating support vector machine.
Statistical comparisons included Kruskal-Wallis test to assess group differences.
MS-PLZC distinguished MCS- from MCS+ with p_adj < 0.05 and large effect size (Cliff's Delta = -0.6178).
MS-LZC showed medium effect size (Cliff's Delta = -0.3067) and lower statistical power.
MS-PLZC achieved highest leave-one-out accuracy (0.733) and ROC-AUC (0.733).

Resumen

BACKGROUND: Accurately 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. METHODS: Resting-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. RESULTS: Both 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). CONCLUSIONS: These 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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