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Updated: Apr 1, 2026
Transauricular Vagus Nerve Stimulation and Electroencephalographic Assessment in Disorders of Consciousness
Published on: July 11, 2025
Analysis of Electroencephalogram Characteristics in Patients with Varying Degrees of Disorders of Consciousness
Yanhua Shi1,2, Siyu Long1,2, Wenjun You3
1Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, 226001 Nantong, Jiangsu, China.
Background:
The subjective limitations of neurobehavioral assessment cause a high misdiagnosis rate for disorders of consciousness (DoC). The purpose of this study was to identify the DoC level based on an analysis of multi-dimensional electroencephalogram (EEG) signals to assist with establishing a clinical diagnosis.
Methods:
Sixty-seven patients with DoC [coma, n = 19; vegetative state (VS), n = 23; and minimally conscious state (MCS), n = 25] were included to analyze resting state EEG characteristics. The EEG features were statistically compared among five band powers (delta, theta, alpha, beta, and gamma) and five brain regions (prefrontal, frontal, parietal, temporal, and occipital) by multidimensional analyses, including time-domain analysis, spectral analysis, and functional brain connectivity.
Results:
Amplitude-integrated electroencephalography (aEEG) center amplitude showed significant differences between coma and MCS (p = 0.02688), with no significant differences observed for the other comparison. Spectral analysis revealed that delta and theta power decreased with higher consciousness levels, whereas alpha, beta, and gamma power increased. Relative power differed among groups across specific brain regions (prefrontal, frontal, parietal, temporal, and occipital) and frequency bands. Weighted Phase Lag Index (wPLI) based functional connectivity demonstrated frequency-specific network reorganization with theta band connectivity strongest in VS and alpha/beta/gamma band connectivity enhanced in MCS. Absolute power topographic maps showed expanding high-power regions from coma-to-MCS in high-frequency bands and the left dorsolateral prefrontal cortex (DLPFC) (F3 electrode) exhibited a consistent power gradient of coma < MCS < VS across all bands.
Conclusions:
Multidimensional EEG features have significant value in differentiating the levels of consciousness disorders. aEEG center amplitude discriminated MCS from coma; delta/gamma relative power separated VS from MCS, and alpha/beta relative power separated coma, VS, and MCS. Parieto-occipital connectivity matrix in the theta band distinguishes coma from VS, while absolute power topography of the left DLPFC shows potential for grading levels of impaired consciousness. These electrophysiologic biomarkers complement behavioral assessments, enhancing diagnostic accuracy.
