Long-range linear correlations and nonlinear chaos estimation differentially characterizes functional connectivity and organization of the Brain EEG

Abstract

We obtained and compared linear and nonlinear estimation of synchronicity and internal organization of a set of electroencephalographic (EEG) time series recorded during the realization of cognitive tasks. Linear estimation consisted of the assessment of Spearman R correlations between pairs of EEG electrodes, andnonlinear estimation consisted in apply rescaled (R/S) analysis on the EEG signal to obtain the Hurst exponent which estimates the degree of predictability (order) of the signal in an unpredictable (chaotic) ongoing background activity. We found adifferential characterization of the coupled synchronicity between pairs of scalp electrodes and the estimation of chaos/no-chaos global balance of the signal. While H increases indicating a tendency towards persistent self-similarity (affinity) and self-organizing processes, the amount of pairs of electrodes highly correlated (R>0.85) diminish, suggesting a sort of counteracting dynamic of balancebetween spontaneously driven homogenizing long-range linear correlation tendency againstan inner brain mechanisms leading to persistent self-organization, information richness and heterogeneity.