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

Abstract

Electroencephalography (EEG) signals depict the electrical activity that takes place at the surface of the brain and provide an important tool for understanding a variety of cognitive processes. The EEG is the product of synchronized activity of the brain, and variations in EEG oscillations patterns reflect the underlying changes in neuronal synchrony. Our aim is to characterize the complexity of the EEG rhythmic oscillations bands when the subjects perform a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment. We estimate the intrinsic correlational structure of the signals within the causality entropy-complexity plane H×C, where the enhanced complexity in the gamma 1, gamma 2, and beta 1 bands allows us to distinguish motor-visual memory tasks from control conditions. We identify the dynamics of the gamma 1, gamma 2, and beta 1 rhythmic oscillations within the zone of a chaotic dissipative behavior, whereas in contrast the beta 2 band shows a much higher level of entropy and a significant low level of complexity that correspond to a non-invertible cubic map. Our findings enhance the importance of the gamma band during attention in perceptual feature binding during the visuomotor/imagery tasks.

Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach

“…Bandt and Pompe (BP) proposed a novel methodology that comprises in changing the signal, by means of a symbolic methodology, into a sequence of patterns and then making inference over them [38][39][40]. In view of the evaluation of the ordinal structures present in the time series and their neighborhood impact on the related probability density function they include the signals' own temporal causality through a methodology of simple application and direct estimation [38][39][40][41][42]. Thus, the BP approach permits us to find important causative data associated with the hidden nonlinear variables that regulate the system.…”

“…In any case, between these two limits there is a wide scope of ordinal structures of important stochastic nature. The complexity measure has been effectively utilized in perception and portrayal of various dynamical regimes [38][39][40][41][42][43]. The non-linear elements of the cerebrum are of dissipative nature, and subject to nonequilibrium conditions that describe the developing properties of the neurons and portray the conduct of the neuronal capacities.…”

“…The Jensen-Shannon divergence, which evaluates the contrast between (at least two) probability distribution functions (PDFs), is particularly valuable to compare the symbol-composition of different sequences. Statistical complexity enables us to measure basic features about the dynamic of the PDF related to the EEG recorded activity [38][39][40][41][42][43]. This measure originally obtained from Information Theory enables us to evaluate the non-linear dynamics of the electro-cortical responses [38][39][40][41][42][43].…”

“…Statistical complexity enables us to measure basic features about the dynamic of the PDF related to the EEG recorded activity [38][39][40][41][42][43]. This measure originally obtained from Information Theory enables us to evaluate the non-linear dynamics of the electro-cortical responses [38][39][40][41][42][43]. The statistical complexity is the result of two entropies, the Shannon entropy and Jensen-Shannon divergence, however it is a non-trivial mathematical relation of the entropy since it relies upon two probability functions, i.e., the one relating to the condition of the system and the uniform PDF taken as reference state.…”

Introduction to Focus Issue: Nonlinear dynamics of non-equilibrium complex systems

“…The EEG is the product of synchronized activity of the brain, and variations in EEG oscillation patterns reflect the underlying changes in neuronal synchrony. In a contribution, Baravalle et al (2018) provide a quantification of the degree of complexity of different brain rhythmic oscillations of the EEG signals, by means of information theoretical approach. Their aim is to characterize the complexity of the EEG rhythmic oscillation bands when the subjects perform a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment.…”