Visualization of EEG brain entropy dynamic maps in basal resting state conditions

In this report we explore the use of a method to generate dynamic brain images that represent the variation of the entropic organization of the cerebral cortex during a particular state or condition. The work uses the derivation of nonlinear estimators of the order/chaos balance obtained by calculating the Hurst exponent from the EEG signal captured by an electrocephalograph. The procedure generates entropic estimators of the EEG time series oscillation, for each electrode, being able to obtain consecutive images of individual maps, which represent the nonlinear average of the EEG order/chaos balance for each 125ms of recording. For the investigation, a database of EEG recordings from a sample of thirteen subjects (N=13) was used to construct a sequence of consecutive entropic brain maps from which a video was finally obtained showing the dynamic variation of the order/chaos structure of the cerebral cortex, for the low beta (13-21Hz) and high beta (22-30Hz) sub-bands, during 3 minutes of EEG recording in basal resting conditions, with eyes closed. The results allow us to appreciate the diversity of states of variable entropy through which people's brains pass from moment to moment. As well as the diversity of different entropic patterns found at the intra- and inter-individual level. A first approximation to the wide variety of entropic quasi-equilibrium patterns shows that, within the wide variability, there are some patterns that are repeated at the sample level, with a variable presence of these, at the individual level. The percentage distribution of the order/chaos balance on the brain surface can be quantified in its relative specific magnitude by calculating the percentage representation of the different color codes that correlate with the order/chaos topography drawn from nonlinear estimators applied to EEG segments of 1/8s duration. (C) 2021 The Authors. Published by Elsevier B.V.