EEG Microstate Comparative Model for Improving the Assessment of Prolonged Disorders of Consciousness: A Pilot Study

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance