FFT vs wavelet prior to connectivity analysis

Dear Mike,

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?

Thank you.

Hi Brian. I do indeed prefer wavelet convolution, also for resting-state data. But it’s mainly personal preference. You can still use FFTs and sleep soundly knowing you’re doing the right thing.

See this video for more.

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