Dear Forum, I’m following the tutorial on this page for inspecting the time-frequency representation of my epoched data, and have some questions regarding how baseline correction is used.
At the beginning of the tutorial, epochs were made from raw data using baseline correction. Then, TFR was computed without further baseline correction. This all makes sense. However, when plotting the TFR, it seems a specific baseline correction was applied using the response before 0. Why? Is this to emphasize the power change after the event onset, for visual inspection only? Or should I also apply it when computing the TFR (for permutation tests later), even if my epochs have already been baseline-corrected?
I wanted to understand the purpose of this “secondary” baseline correction because I suspect it could change my results. My epochs were initially corrected using a baseline period that has up to a few seconds gap before the epoch onset (epochs are responses to a word in the middle of a sentence, and baseline was the silence period between sentences), therefore the response immediately before the epoch onset is not expected to have a zero mean.
Maybe @drammock or @mmagnuski could answer this one?
I don’t know if their is another goal than to emphasize the power change, and I think you can either:
apply it when computing the TFR
test/visualize a given baseline and then apply it with apply_baseline before tests
In both case, I would apply the baseline before tests, but I’m not certain it is correct and what is the best approach here.
I agree that the reason that the TFR.plot and TFR.plot_topo functions have a baseline and mode params is to emphasize the change in power between baseline period and the rest of the trial.
@mscheltienne is correct that you could use TFR.apply_baseline() to do that step before doing statistics (provided that the correct baseline time span is still present, i.e., you haven’t cropped or shifted the epochs).
As to whether you should do this before clustering — I think generally it’s probably the right thing to do (e.g., we do it in this example: Non-parametric 1 sample cluster statistic on single trial power — MNE 1.6.1 documentation), because generally what neuroscientests are interested in is something like “increase in power over baseline in frequency band X being greater in condition 1 than in condition 2”. In other words, the actual value of the power estimate is usually not what we care about, rather it’s usually the relative power in different bands, conditions, or subject populations.
I think @agramfort has some strong opinions about which method of TFR baseline correction are appropriate, so if you have follow-up questions about that hopefully he can chime in.