Abstract
The probability density functions (pdf’s) and the first order structure functions (SF’s) of the pairwise Euclidean distances between scaled multichannel human EEG signals at different time lags under hypoxia and in resting state at different ages are estimated. It is found that the hyper gamma distribution is a good fit for the empirically derived pdf in all cases. It means that only two parameters (sample mean of EEG Euclidean distances at a given time lag and relevant coefficient of variation) may be used in the approximate classification of empirical pdf’s. Both these parameters tend to increase in the first twenty years of life and tend to decrease as healthy adults getting older. Our findings indicate that such age-related dependence of these parameters looks like as age- related dependence of the total brain white matter volume. It is shown that 15 min hypoxia (8% oxygen in nitrogen) causes a significant (about 50%) decrease of the mean relative displacement EEG value that is typical for the rest state. In some sense the impact of the oxygen deficit looks like the subject getting older during short-term period.
Highlights
The progress in information theory, nonlinear dynamics, deterministic chaos theory, and random fractal theory caused a wave of researches where the analysis of complexity EEG signals is done on the base on the using of various complexity measures derived from them
The maximum value of PRD1 here refers to the case of the original EEG and is less than 8.8% while the maximum relative error in the first three moments does not exceed 0.5%
In each case the empirically derived pdf are fitted quite well by the single hyper gamma distribution. It means that only two parameters may be taken into account
Summary
The progress in information theory, nonlinear dynamics, deterministic chaos theory, and random fractal theory caused a wave of researches where the analysis of complexity EEG signals is done on the base on the using of various complexity measures derived from them. Since the foundations of these theories are fundamentally different one can get a variety of complexity measures concerning the same EEG process. Detailed examination of a number of such measures given in [1] shows that their variations with time are either similar or reciprocal, but behaviors some of them are counter-intuitive and puzzling. The attempt to understand such behaviors is done in [1] through a new multiscale complexity measure of EEG. (2014) Age-Related Changes in Probability Density Function of Pairwise Euclidean Distances between Multichannel Human EEG Signals.
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