[Mne_analysis] compute_covariance

[Alexandre Gramfort]

hi,

> This is probably a dummy question but to assemble the inverse operator in
> the older versions of mne_python, one had to first compute the noise
> covariance then regularize it manually by assigning mag grad and eeg
> regularisation factors. the defaults are 0.1 here.

correct. You can still do this. Although when working on this with
Denis we realized
that the 0.1 value is typically too high. This leads to smaller
whitened data and for example weaker dSPM/sLORETA values.

> At the moment the automated paradigm computes the noise covariance and
> regularizes in the same framework by cross-validation. the defaults of
> regularisation factors for empirical approach and fixed diagonal are much
> less than the old ones (0.01, 0.01 and 0.0 for grad, mag and eeg
> respectively).

when doing cross-validation the parameters are optimized for your data.

you can use the evoked.plot_white method to check your whitening quality.

> I had a look at the new cov.py code to see if can see some scalings that
> could explain why the new regularisation uses smaller defaults but seems
> that both the old and new versions use the same "regularize" function for
> empirical and fixed diagonal.
>
> I was wondering if you could please let me know where am I making a mistake?
> and if I'm not making a mistake then why the regularisation factors are so
> different in the old and new versions?

check with evoked.plot_white the quality of your noise covariances
(old and new ones).

you can share the plots if you need feedback.

HTH
Alex