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First, an MEMD-based bivariate phase synchrony measure is defined for a more robust description of time-varying phase synchrony across frequencies.
Phase synchrony analysis is a useful measure of linear dependence between two stochastic signals.
These time-varying measures of phase synchrony using wavelet or HTs are similar in their results.
Some authors used HT to measure phase synchrony between two signals [37, 38, 40].
This interpretation justifies narrow-band filtering in the case of phase synchrony.
This is a very useful characteristic for cross-spectral and phase synchrony.
Although the wavelet and STFT-based phase synchrony approaches consider the nonstationarity issue, they suffer from a number of drawbacks.
Moreover, phase synchrony can be used for non-periodic and for chaotic signals such as EEG [8, 39].
Both give a sharper phase synchrony estimates over time and frequency, especially at the low frequency range [46].
Thus, the concept of phase synchrony helped to measure the synchrony evolution while the amplitude of the signals remained uncorrelated.
Some authors used wavelet-based approach to analyze phase synchrony between pairs of EEG signals [13, 43 45].
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