- MNE-Python version: 0.23.0
- operating system: CentOS 7
Hi, recently I am trying to perform source localization with MNE. Thanks for the amazing tutorials on relevant topics and I have seemed to reconstrcuted things successfully with a template MRI.
However, since I have a very long time series, reconstructing with the whole brain would instantly kill my computer. I decided to select specific ROI and reconstruct the source only from them. For instance, let’s say the caudlamiddlefrontal-lh from the aparc parcellation.
The command is pretty standard, labels[roi_idx] is the corresponding ROI:
stc = apply_inverse_raw(raw_file, inverse_operator, lambda2, label=labels[roi_idx], method='dSPM', pick_ori=None, verbose=None
The command executed successfully while generating both ****-lh.stc and ****-rh.stc. I checked both file, there were data in ****-lh.stc but no data in ****-rh.stc (which is the expected behavior i guess). So, I deleted all ****-rh.stc file manually. Then, performed the whole thing again for caudlamiddlefrontal-rh, and deleted all ****-lh this time.
After that, I used mne.read_source_estimate(****-lh.stc) from the folder storing only the ****-lh.stc and there was an error stating that there were missing files for another hemisphere. So, I put all ****-lh.stc and ****-rh.stc in one folder and it works.
So I wonder:
- If we can use apply_inverse_raw with multiple labels at once? Such that we do not have generate source files for both hemi and delete the one that was not included everytime.
- If we can use read_source_estimate with file from a single hemisphere?
Thank you very much for the amazing package!