• Wen Shi, Yamin Li, Zhian Liu, Jing Li, Qiang Wang, Xiangguo Yan, and Gang Wang.
• The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, Shaanxi 710049, P. R. China.
• Int J Neural Syst. 2020 Feb 1; 30 (2): 2050005.

AbstractDynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert-Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.

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