A Dynamical Phase Synchronization Based Approach to Study the Effects of Long-Term Alcoholism on Functional Connectivity Dynamics.

The paper attempts to investigate the changes in the brain network dynamics between alcoholic and non-alcoholic groups using electroencephalographic signals. A novel entropy-based technique is proposed in this study to understand the dynamics of the neural network for the two groups. To do this, we have examined for the two groups their time-varying instantaneous phase synchronization events when the subjects are engaged in an object recognition task. Next, we have characterized the complexity of the phase synchronization using Fuzzy Sample Entropy over different time scales, referred to as Multiscale Fuzzy Sample Entropy (MFSampEn). The temporal dynamics of phase synchronization arise due to the presence of deterministic characteristics in the time series and the results of surrogate analyses confirm that. Lastly, we check the applicability of Multiscale Fuzzy Sample Entropy (MFSampEn) as entropy-based features in the context of alcoholic and non-alcoholic object recognition classification.