Influence of Time-Series Extraction on Binge Drinking Interpretability Using Functional Connectivity Analysis

Brain connectivity analysis has gained considerable importance in different cognitive tasks and the detection of pathological conditions. Despite recent advances in connectivity analysis, there are still problems to be solved, being a proper extraction of the time-series to characterize the regions of interest (ROI) one of the challenges. In this work, we examine the influence of the time-varying mean estimation on the brain connectivity analysis for control and binge drinkers subjects. The obtained results show that the performance of brain connectivity improves using the eigenvalue-based averaging since it may face better the nonstationarity behavior and inter-trial variability of MEG activity.