HHT-Microstate Analysis of EEG in Nicotine Addicts

Abstract

Background: Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. Method: Combining Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. Purpose: To study the difference of EEG Microstate parameters of nicotine addicts at each frequency band under HHT-Microstate method. Result: By using HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the traditional methods, the similarity index of microstate topographic maps A2, A5 and B4 at alpha and beta bands have significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. The occurrence per second (OPS), time coverage ratio (TCR) and global explained variance (GEV) of microstate D5 at delta band in smoke group are lower than those in neutral group, the OPS, TCR and GEV of microstate A2 at alpha band are higher than those in neutral group, and the TCR, GEV of microstate B2 at beta band are higher than those in neutral group. Finally, the microstate parameters at delta, alpha and beta bands obtained by the HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The accuracy is 93%, which can effectively detect and identify different types of addiction diseases. Conclusion: The frequency band Microstate obtained by HHT-Microstate method was used to retain the time information in the spectrum domain. With the significant differences in EEG microstate parameters between the two groups of nicotine addiction and the higher classification accuracy, HHT-Microstate analysis can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.