Dear MNE community,
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.
The parameters used are as following:
import mne from mne.time_frequency import tfr_morlet epoched_data = epoched_data freqs = np.around(np.arange(1, 31, 0.5),1) n_cycles = freqs / 3 power = tfr_morlet(epoched_data, freqs=freqs, n_cycles=n_cycles, use_fft=True, return_itc=False, decim=3, n_jobs=1)
- MNE-Python version: 0.21.2
- operating system: Win10