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Background <p>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.</p> Methods <p>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.</p> Results <p>Both MS-LZC and MS-PLZC showed statistically significant group differences (Kruskal-Wallis test: MS-LZC: H = 26.92, <i>p</i> &lt; 0.0000, η²=0.2099; MS-PLZC: H = 35.11, <i>p</i> &lt; 0.0000, η²=0.2816), with MS-PLZC exhibiting greater statistical power. Notably, MS-PLZC successfully distinguished between MCS- and MCS+ patients (p _adj &lt; 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).</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Zhibin Zhao,
  • Zhenhu Liang,
  • Yong Wang,
  • Xiaoli Li,
  • He Chen

摘要

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.