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Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.