Hurst Entropy Profiles for Beta Low and Beta High Eeg Sub-Bands Part I: Intragroup Comparison

The non-linear exploration of the entropic structure of the information contained in an EEG allows access to a new source of data that characterizes, in a more global way, the functioning of the brain as a whole. On the understanding that the brain is a hyper-complex system, describing it only through a linear statistical approach, mainly based on the comparison of differences in the magnitude of the power of the electroencephalographic signal between comparable conditions, leaves incomplete the part of the brain phenomenon that obeys non-linear laws. The present work explores the possibility of characterizing the entropic structure of a human brain, through the construction of order/chaos balance profiles. We proceeded to study the entropic content of the EEG signal, by estimating the Hurst exponent in segments of different sizes, for a set of data from 14 EEG electrodes (Emotiv Epoc Research Edition), taken in basal resting conditions, during 3-5 minutes with eyes closed. The EEG recordings of thirteen subjects (N=13) were studied for the low beta (13-21Hz) and high beta (22-30Hz) sub-bands, in four time-processing windows: 125ms, 250ms, 500ms, and 1s. In addition to obtaining the traditional Hurst estimator, the behavior of two derivatives of the Hurst exponent: the moving-Hurst (muHurst), and the mHurst, an estimator interpreted as a global entropic trend of the time series, were also evaluated. The results showed a clear differentiation between the entropic profiles obtained for low beta and high beta, for all individuals, as well as a different entropic structure of both sub-bands, when comparing Hurst exponents and their derivatives with different time processing windows. At the 1s scale (128 EEG frames), the average entropic content of the time series (muHurst) is relatively higher than the entropic load contained in the mid-term estimation of the whole signal (H(trad)). At the same time, estimates of mHurst, a derivative that assesses the overall entropic trend of the signal, indicated mostly lower entropic (more organized) content than estimates at shorter timescales. Our nonlinear exploration of the entropic structure of the brain revealed a wealth of details that can be used to complement the traditional characterization of the brain that linear approaches have provided so far. Our procedure allowed us to discern substantive differences between the entropic characteristics of the low beta and high beta EEG sub-bands. As well as individual differences within the group, potential substrates for the search of neural markers of psychological, pharmacological, or clinical utility. © 2021 The Authors. Published by Elsevier B.V.