I have a question about preparing data for connectivity analysis (phase-based).
I am working with resting-state EEG (so no time-related events of interest). Prior to running connectivity, I first need to calculate the complex Fourier spectra from the data to obtain the phase information. I note in your videos, that you typically use a wavelet-based approach to do this. I understand that the benefit of this is that it keeps the ‘time’ information which is useful for task-based EEG etc. However, as time info is generally not important for rest EEG, I wonder if it would be appropriate to use a Fourier transform instead (i.e., to calculate phase over the entire epoch)?
I’m curious if there is much of a difference between these two approaches for rest EEG? For example, taking the Fourier transform versus using a wavelet convolution (and then subsequently averaging over time) before calculating connectivity? I would presume that due to some unusable periods (e.g., edge effects etc) the wavelet-based approach would lead to output over a smaller section of the epoch, but apart from that results would be quite similar?