Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia

Background Subclinical insomnia (sINSO) represents an early stage of insomnia but lacks effective biomarkers for its recognition. The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO patients and healthy controls.Methods Resting-state EEG data from 20 sINSO subjects and 20 healthy controls, under both open and closed eye conditions, were analyzed using microstate clustering (labeled A, B, C, and D) and machine learning to evaluate their discriminative power.Results The microstate global explained variance of the eyes-closed data was better than that of the eyes-open data. In the sINSO group under closed-eye conditions, the tendencies and transition probabilities for microstate changes are as follows: A to D at 7.7%, B to D at 10.7%, C to A at 7.3%, and D to B at 10.8%. Under open-eye conditions, they are: A to C at 9.1%, B to C at 8.4%, C to D at 9.4%, and D to C at 8.9%. Machine learning classification showed higher accuracy in closed-eye conditions, reaching 77.6%.Conclusion Resting-state EEG microstates exhibit significant differences between sINSO and healthy individuals. These microstates are promising biomarkers for distinguishing sINSO, with closed-eye data providing the most reliable discrimination.