Improved HHT-microstate analysis of EEG in nicotine addicts

Abstract

BackgroundSubstance 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.MethodsTo study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts.ResultsAfter using improved 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 FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods.ConclusionThus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

“…These findings extend previous studies on topographies of frequency-decomposed microstates (Musaeus et al, 2020; Férat et al, 2022; Terpou et al, 2022; Xiong et al, 2023). Férat et al (2022) and Terpou et al (2022) fitted broadband microstate maps directly to all other frequency bands, which might neglect distinct topographic configurations in a specific frequency band.…”

“…Musaeus and colleagues (2020) defined a four-cluster solution (k = 4) and reported different topographies across different EEG frequency bands. More recently, Xiong et al (2023) defined the optimal number of clusters based on GEV and cross-validation criterion (Koenig et al, 2014). They reported that this number varies with frequency bands for task-state EEG, which enabled participants to focus on the smoking-related or paired neutral pictures.…”