If you have a question or issue with MNE-Python, please include the following info:
- MNE-Python version: 0.23.0
- operating system: MAC M1
I am doing source localisation from EEG, and was having a methodological doubt about the correctness of the way I calculated the noise covariance matrix (from epochs).
My dataset is composed of reconstructed epochs from the segmentation of 10sec of EEG signal that follows verbal instructions of motor commands (5*2sec epochs, the 1st epoch corresponding of the “offset” of the -verbal- stim). My epochs are therefore 2sec cropped and correspond to either “move” or “stop moving” (rest) instructions.
Considering this setup, 2 questions came up to my mind concerning the calculation of noise covariance :
- given that I am trying to look for particular activations following « keep moving » instructions, is it correct (and recommended) to use “rest” epochs as noise, and therefore to calculate the noise covariance from these rest epochs? One precision, I calculate the inverse
- Is it a problem if my epochs are cropped ? Should I instead construct new epochs (110sec instead of 52sec) so that I can use the baseline ? If there is another solution, I would prefer not to as I’ll use the 2sec cropped epochs for the source localisation.
For the moment I could obtain a source localisation, but using :
noise_cov = mne.compute_covariance(epochs['stop'], method='auto')
as my noise covariance, so that the « stop moving » (rest) epochs are considered as noise.
Following these questions about covariance, I calculated the forward and inverse solution for the whole set of epochs (move AND rest) to construct a global model of the task, but applied it to “move” epochs only. In this case, should I calculate everything based on ‘move’ epochs only?
Sorry for this long message, but I am not that confident about the correctness of doing it this way (methodologically as well as theorically), and wanted to make sure that I am not introducing any error.
Would you have any recommandations about this ?
Thanks a lot for your consideration, any comment would be of a huge help !