I’m new to EEG time-frequency analysis using MNE-python and I’m having a problem with the temporal resolution of my data.
So, I have some epoched EEG data from -1000ms (prestimulus) to 1996ms (post-) and at a sampling rate of 250 Hz, i.e. having 750 time-points in the array, precision = 4ms.
However, after applying the mne.time_frequency.tfr_morlet() function, the output time-frequency representation seemed has a lower sampling rate (250/3 Hz), with only 250 time-points, precision = 12 ms. The length of the epoch was unchanged though ([-1000, 1988]).
I’m wondering why this problem happened and how can I fix it, i.e. keep the original temporal resolution in the tfr output.
Awesome! How did you come up with decim=3 in the first place? Did you try to follow one of our tutorials and copied the code that was used there? I’m just asking because maybe we need to clarify things a little in that respective tutorial to avoid such confusion in the future! Thank you very much.
It should be said though that I also checked the document of the tfr_morlet() function several times about its parameters, but seems I misunderstood the notes on decim and thought it was rounding the power value in the array to a certain decimal place. (mne.time_frequency.tfr_morlet — MNE 0.22.0 documentation)