Hello,
Does anybody have thoughts on what kinds of preprocessing the emptyroom
data must undergo before computing the noise covariance matrix for source
modeling?
An obvious suggestion is to subject the emptyroom data to identical
preprocessing steps (SSS with identical parameters, or some projection,
before source modeling). How far can we take this? For instance, if one
wants to estimate oscillatory activity at the cortical level for a narrow
freq. band (which has very different noise characteristics from the entire
band of the recording), should we compute the noise covariance from
filtered emptyroom data?
More generally, if one is analyzing several freq. bands, is it worthwhile
estimating the noise covariance (and thus, a separate inverse operator)
for each frequency band?
Regards,
Pavan