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S-estimator, as other measures of synchronization [54], [55], is affected by EEG power.
Indeed, recent work in nonlinear dynamical systems resulted in new application-independent multivariate measures of synchronization [32], [33], [34], [35].
As other measures of synchronization [54], [55], the S-estimator is affected by the amplitude of the EEG signals.
Although several measures of synchronization have been introduced, particularly for studying chaotic systems, only a few have been widely adopted in neuroscience.
Nevertheless, being used in the analysis of the same data, various measures of synchronization detect coupling, although with different sensitivity [33], [56].
In particular, information-based measures of synchronization can reveal non-linear relationships between the time courses of complex oscillators, and can even reveal directionality of influence in their time series (e.g., [62]).
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The measure of synchronization strength range from 0 to 1.
Let us introduce a formal measure of synchronization of two signals by. (11).
The S-estimator was proved to be a robust and easily scalable multivariate measure of synchronization, highly sensitive even with a reasonably small amount of data.
Collective behavior was classified as stable, switching, and unstable according to a combined measure of synchronization index (SI) [63], [64] and dwell time τ around regions near in-phase.
In addition, our measure of synchronization benefits from the fact that volume conduction inflates synchronization indices toward the maximally possible values.
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