I’m working on a time-frequency EEG analysis with MNE-Python with quite long epochs (9.6 seconds). I was wondering if baseline correction is recommended in this context, and if yes, at which point in the analysis is it best to do so: before or after the time-frequency transformation ?
This is what I know so far about baseline use:
In evoked potentials (ERPs), baseline correction helps to eliminate variations in the initial baseline potential, ensure a stable reference and centers ERP variations around zero. It allow to enable deflections to be interpreted more easily.
In time-frequency (TF) analysis, it seems that baselines are not always necessary. This is because oscillations are continuous, not strictly dependent on a resting potential, and spectral power measures relative amplitudes rather than absolute variations in potential. That said, a baseline can sometimes be used in TF to normalize spectral power or to control power variations between trials.
I understand that this can depend on the nature of the data, but given the length of my epochs, I’d like to know what the best practices are for reliable time-frequency analysis with long epochs.
Hey,
this is a very late reply, but maybe it will help someone.
Opinions on baseline correction in both ERPs and TF analysis are quite diverse (even in the realm of ERPs, opinions are diverse, see around the 8th minute here: https://www.youtube.com/watch?v=2wS7-XILNso).
You are right that baseline correction is not always necessary. Actually, baseline correction in TF analysis is usually used for visualization only, statistical analysis is still performed on non-baseline corrected data.