Hi,
I have noticed that the tutorials use stc_near_sensors function for computing source estimates with sEEG or ECoG data. How is it different from minimum_norm.apply_inverse or other conventional approaches? Is there any documentation on this?
Thanks.
Yoga
Hi Yoga, stc_near_sensors take a space of source vertices and uses the center of an intracranial electrode contact to find the nearest vertex to that point. The minimum norm estimates are implemented for MEG and EEG and take data from the scalp and find a maximally likely source estimate using boundary element modeling. Application of boundary element modeling isn’t implemented yet for intracranial data, unfortunately; see [ENH] Finite Element Modeling for Tissue Displacement by Intracranial Electrodes and Conductivity · Issue #10216 · mne-tools/mne-python · GitHub.
Hi Alex,
Thanks for your response and the clarification. I look forward to more updates on this issue.
Best,
Yoga