Dear all,
Apologies for resurrecting this old thread, but I want to do something similar and double check whether the approach makes sense.
On the scalp level, I'm computing regressions between each EEG datapoint (i.e. each sample and channel of each single trial) and a behavioral variable. The resulting beta values yield some interesting spatial and temporal patterns, which I would like to localize at source level. Can I treat the betas the same way as raw amplitudes and perform a dSPM or sLORETA sourceloc with them?
I'm asking since I read on other brainstorm threads that source localization is not correct after Laplacian transform, as '...the minimum norm estimates requires the EEG in the original physical units.' (quote from Francois) and 'The source maps can only be legitimately obtained with the original sensor data. Doing otherwise would violate the physics of forward/inverse modeling and yield invalid results.' (quote from Sylvain)
Any help would be very much appreciated!
Ulrich