Thank you Denis, it was helpful!
I tried it with
cov = mne.compute_covariance(allepochs, method=['empirical', 'shrunk'] ,
tmin=-0.8, tmax=0.0 , return_estimators=True, verbose=True)
The best was the ?shrunk?. However, the GFP is lower then 1 for the
?shrunk? and is close to 1 for ?empirical? (see the figure). Is it OK?
Elena
Hi Elena,
It looks like you are the entire time window? If you do this because you
have cropped epochs for the purpose of cov estimation then the idea of this
plot is to inspect the data segments that were not part of the window used
for cov estimation.
The plot further suggest that you are using data processed with SSS which
are rank deficient. Factor Analysis is not expected to work well. For SSS
the "shrunk" option should do a good job. I would run it with
method=('empirical', 'shrunk') and return the estimators (see parameter) to
compare them. One should always compare the fancier estimators with the
empirical covariance. In that case you would pass a list of covariance
objects to the plot_white method which will then show you one time series
per covariance.
I hope this helps,
Denis
Hello,
I calculated noise covariance matrix on baseline using method=?auto? to
find an optimal regularization:
cov = mne.compute_covariance(covepochs, method= ?auto? , tmin=None,
tmax=None , verbose=True)
The optimal was ?factor analysis?, but it gave me unacceptable solution.
When I look at whitening, it seems that ?shrunk? works better (see the
figures attached)! What can be the problem?
Elena
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