Hello everyone,
I am currently working on performing source reconstruction on linear regression parameters (beta coefficients) of MEG data.
I could not find anything on the subject on the mne docs, even though I know from discussions people are able to do it.
The closest I could find here is : https://mne.discourse.group/t/computing-regression-on-sensor-data-then-transforming-to-source-space/, but it is from 2014.
I would like to know what the best practice is in this case, namely with regards to what kind of noise covariance matrix is expected, and the impact of the regularisation parameter.
I’ve tried doing the source reconstruction using the empty room noise covariance matrix, but it does not seem to make much sense.
What kind of procedure would you recommend ?
Best regards,
Merlin Dumeur