- MNE-Python version: 0.23
- operating system: WSL2 Ubuntu 18.04
I am interested in creating a more detailed BEM to do source localization with SEEG. There is evidence that adding CSF, gray matter and white matter conductivities could greatly improve localization for depth electrodes (where as you might not care for scalp EEG). From reading the forum posts (e.g. Customized head/forward model - #2 by system, it seems the way to do this is with MNE-Openmeeg. Specifically modifying demo-openmeeg.py to add the following domains:
Gray matter (cortex and deep)
I will bring in my own surface files for these regions - any great ideas of best way to generate? I have a 5TT image from FSL, so maybe use FreeSurfer’s mri2surf?
My main question is what difficulties do you expect?
Will the rest of the pipeline to create the forward/inverse solution be ok with this custom BEM?
There will be multiple enclosed sub domains for each tissue type (e.g. cortex and deep gray, different ventricles), will this be a problem?
Anything else I should be aware of?
Bonus question: what inverse method do people think I should use for SEEG? dSPM doesn’t seem to localize my 5mA stimulation pulses too well (concerning), and the LORETA algorithms localize the stimulation very well, but do appear very smoothed as is known to be a problem. Any thoughts would be great.