Moving Correlations and Chaos in the Brain During Closed Eyes Basal Conditions

In this work we explored the use of linear and nonlinear, statistical approaches to studying basal EEG brain activity. To do this, we used the Moving Correlation Coefficient (MCC), and the Hurst exponent estimator to visualize short and long term EEG behavior during 2 minutes of EEG recording in closed eyes basal conditions. The EEG of 8 subjects served as data source for exploring the time series organization of the EEG in resting state. We found inter-subjects shared network patterns of high synchronic functional connectivity in frontal and right temporo-occipital regions. We also found a diversity of individual different networks of variable degree of synchronicity in the range of -0.5<R<0.5 (Spearman linear r). The nonlinear approach using Hurst exponent estimator for the chaos/order balance in the EEG time series, gave us averages values of H >0.5 for all channels. The short-term approximation to the chaos oscillation (M-Hurst) revealed several individual differences and a potential tool for subject s characterization. © 2018 The Authors. Published by Elsevier B.V.