Entropy-Complexity Characterization of Brain Development in Chickens

Abstract

Electroencephalography (EEG) reflects the electrical activity of the brain, which can be considered chaotic and ruled by a nonlinear dynamics. Chickens exhibit a protracted period of maturation, and this temporal separation of the synapse formation and maturation phases is analogous to human neural development, though the changes in chickens occur in weeks compared to years in humans. The development of synaptic networks in the chicken brain can be regarded as occurring in two broadly defined phases. We specifically describe the chicken brain development phases in the causality entropy-complexity plane H × C, showing that the complexity of the electrical activity can be characterized by estimating the intrinsic correlational structure of the EEG signal. This allows us to identify the dynamics of the developing chicken brain within the zone of a chaotic dissipative behavior in the plane H × C.

Rhythmic activities of the brain: Quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

“…1 The electrical activity of the brain can be considered chaotic and ruled by a nonlinear dynamics. 2 The EEG is a non-invasive technique recorded on the scalp that often has a poor relationship to the spiking activity of individual neurons. However, the EEG recorded by a single electrode is a spatially smoothed version of the local field potentials under a scalp surface on the order of 10 cm 2 .…”

“…More specifically, we propose an efficient methodology to quantify the degree of complexity within the different oscillations bands of the electrical activity of the brain recorded through the EEG signals while performing a visuomotor or imaginative cognitive task using the BCI2000 system. 21,34 We precisely quantify the different features of oscillatory patterns considering subtle measures accounting for the causal information: Shannon permutation entropy 2,[35][36][37][38][39] and Martín, Plastino, and Rosso (MPR) permutation statistical complexity. 2,[35][36][37][38][39] Importantly, we used the NSB methodology to remove sample size dependent bias from the entropy and complexity estimations 32 that are estimated using the BP methodology.…”

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach

“…This approach, which employs the Complexity-Entropy plane, has been successfully used in visualization and characterization of different dynamical regimes when the system parameters change, [4][5][6][7][8][9][10][11] optical chaos, [12][13][14][15][16] hydrology, [17][18][19] geophysics, [20][21][22] engineering, [23][24][25][26] biometrics, 27 characterization of pseudo-random number generators, 28,29 biomedical signal analysis (Ref. 30 and references therein [31][32][33][34][35][36][37][38][39][40], and econophysics (Ref. 30 and references therein [41][42][43][44][45][46] ), just to mention a few.…”

Complexity-Entropy Maps as a Tool for the Characterization of the Clinical Electrophysiological Evolution of Patients under Pharmacological Treatment with Psychotropic Drugs

“…Particularly in this article, a parallel analysis of both methods on EEG signals is used, and we present the results on a Complexity vs. Entropy map. This kind of representation analysis has been used in chaos versus noise analysis, text authoring analysis, and electrophysiological evolution of EEG in chickens, among others [8][9][10]. One of the most important novel aspects of the method is the analysis of the same signal through two different perspectives, one statistical (Shannon entropy) and the other deterministic (Lempel-Ziv complexity).…”