Reliability of EEG microstate analysis at different electrode densities during propofol-induced transitions of brain states
Kexu Zhang,
Wen Shi,
Chang Wang,
Yamin Li,
Zhian Liu,
Tun Liu,
Jing Li,
Xiangguo Yan,
Qiang Wang,
Zehong Cao,
Gang Wang
Affiliations
Kexu Zhang
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China
Wen Shi
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; The Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
Chang 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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China
Yamin Li
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Zhian Liu
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China
Tun Liu
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China; Department of Anesthesiology, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
Jing Li
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China; Department of Anesthesiology, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
Xiangguo Yan
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China
Qiang Wang
Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
Zehong Cao
School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7001, Australia
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, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China; Corresponding author at: 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, Xi'an 710049, China.
Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes.In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation.First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition.The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes.The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.
