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Advanced brain function requires different levels of integration and coordination between multi-regional nervous systems, the underlying mechanism is the simultaneous oscillation of various neural networks. EEG is an increasingly method to detect brain function with high temporal resolution and low cost. How to analyze the synchronization phenomenon is the focus of cognitive neuroscience research based on EEG signals. Wavelet coherence is a classical method to evaluate EEG synchronization, but it is uncertain how to use. In this paper, this requires knowledge of the true relationship between signals, hence we compare different measures of functional connectivity on simulated data (unidirectional coupled Henon maps, and the auditory Stroop EEG), including wavelet cross-spectrum, wavelet correlation, wavelet coherence and FFT coherence. To determine whether synchrony is detected, surrogate data were generated and analyzed, and FFT coherence measures performed best on simulated data. Above all, the parameter optimization method of the wavelet cross-spectrum is proposed with many samples. It is found that the optimized wavelet coherence performed most reliably than FFT coherence.

A Best Detecting Synchrony Method in Audio STROOP EEG Based on Wavelet Coherence