In this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive,eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.
This article deals with the complexity aspects of the recorded electroencephalogram (EEG) signal from male and female subjects. The analysis follows direct application of time series measures of global linear complexity and characterization of the embedded complexity in the signals using the nonlinear statistic of approximate entropy. The study reveals significant differences in complexity between the two sex groups during passive, no-task conditions, whereas no apparent variation exists during a mental task state. The detection of subtle changes as well as the ease in presenting a global picture of the complexity variation on the human cortical surface makes the nonlinear statistic a better marker of system complexity.
A study of the effect of time scales in brain dynamics on the unfolding of the attractors in the phase space, reconstructed by a time delay embedding of the EEG signal, was carried out. Applying the techniques of nonlinear time series analysis, the unfolding rate of the system attractor was determined by analyzing the variation of the correlation dimension parameter and subjecting it to a bi-parametric fit. The behavior of the parameter, which measures the rate of unfolding, was monitored for varying time scales in two cases: (a) normal eyes closed condition and (b) the pathological case of epilepsy. Significant results were obtained.
The present study observed the coordination between cortical areas during no task conditions as well as for the pathological condition of epilepsy, by application of the phase synchronization technique to the EEG signal in a multichannel recording. The index obtained from the phase entrainment investigation was properly scaled by a novel method to take into account the effect of nearest neighbor interactions. This scaled index was analyzed temporally to learn about the behavior of regional interactions in time. The results obtained not only corroborate earlier known results, but also give deeper insight into actual brain functioning.
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