I want to create the trans file for my cohort. All the analysis will be run in the subject native space so I will have one trans file per subject. I am using mne.gui.coregistration() to import the subject’s electrode positions
For coregistration, you want to use the high-density scalp surface from mne_make_scalp_surfaces. The next step is to align the fiducial points (LPA, RPA and nasion) … they look completely off in your coregistration. Once that is aligned, you may further fine-tune using the electrode positions. Note that it’s not totally unreasonable if the positions are slightly above the scalp since the EEG electrodes slightly above, but your current co-registration does not look correct.
I’ve run mne_make_scalp_surfaces for one subject and get the high-density scalp surface.
Re: aligning the fiducials, i am not quite sure what do you mean and how to actually do it. I re-do the montage and projected back in the new surface. Making the high-density scalp surface doesn’t seem to have resolve the issue
So I guess the problem can be the fiducial alignment. Any insight on how practically solve this?
@agramfort Yep 100% sure it is the MRI of the subject. For being absolutely clear this is another subject to the one that I posted before but same story/problem
There may be some variation in how different labs report these points, but you need to mark the same landmark that was marked during the experiment.
Once the fiducials are aligned, the ICP algorithm is likely to be helpful for fine-tuning.
Thanks @mainakjas and @agramfort. I followed the video tutorial but I think the problem is that the electrodes x,y,z coordinates do not match with the head shape. They seem to form a shape that is larger than the subject head
Your best bet is to contact the authors of the dataset asking them for more information. I would not recommend hacking and moving forward without understanding what is going on. The fiducials need to match as a first pass … otherwise something is wrong. You could also try another subject to check if it is a systematic problem in the dataset or a one-off.