I am using mne_make_movie to make .stc files of current estimates. All the
examples I've seen so far use mne_make_movie with one inverse operator
(specified with --inv) and one MEG average file (with --meas). But in my
case, my average .fif file for each subject contains four separate
averages, one for each condition (so for example if i open mne_analyze,
load a surface, and load the .fif file using File/Open, I then pick one of
the four sets to display). So my question is, instead of running
mne_make_movie once per subject to make one .stc file, would the
appropriate procedure be to do it four times per subject, to get separate
stc files for each condition (using the --set option to specify which
condition to get from the .fif)?
Sorry if this is a basic question addressed in the manual somewhere; maybe
i just missed it. And thanks to everyone on the list who has helped me
before, I appreciate it very much!
So my question is, instead of running mne_make_movie once
per subject to make one .stc file, would the appropriate procedure be to do
it four times per subject, to get separate stc files for each condition
(using the --set option to specify which condition to get from the .fif)?
I would say so. What do you think would be a more convenient way ?
Here is the manual section. It may not be explicit enough in its
explanation, but --set is what you are looking for. If you can come up
with a better wording, you can re-write it through github. The entire
manual is run through the mne-python project on github.
6.5.2Input files
--inv <name>
Load the inverse operator decomposition from here.
--meas <name>
Load the MEG or EEG data from this file.
--set <number>
The data set (condition) number to load. This is the sequential number
of the condition. You can easily see the association by looking at the
condition list in mne_analyze when you load the file.
Thanks, Alex and Dan! That is what I figured, I just wanted to make sure I
was doing the right analysis. (And don't worry, it's not inconvenient--I
can let a script do it all for me!)