[Mne_analysis] MNE method is invariant to the scale of noise

[Alexandre Gramfort]

if you change noise scaling you should change the SNR as it means you have
a lot worse SNR. the regularization parameter lambda is a function of the
snr
as you can see in the script.

dSPM and sLORETA are like t-stat or f-stat. So if you multiply the cov by
1e6
then you will have dSPM and sLORETA solutions which are just divided by 1e3
(the scale of the standard deviation).

HTH
Alex




On Wed, Feb 14, 2018 at 8:33 PM, Evgenii Kalenkovich <
e.kalenkovich at gmail.com> wrote:

> Hi all,
>
> I noticed that if I scale the noise covariance matrix, it does not change
> the source estimates in any way. In this example
> <https://martinos.org/mne/stable/auto_tutorials/plot_mne_dspm_source_localization.html#sphx-glr-auto-tutorials-plot-mne-dspm-source-localization-py> from
> the example gallery if I change  the method to "MNE" before the first stc
> calculation and then do this:
>
> inverse_operator = make_inverse_operator(info, fwd, noise_cov,
>                                          loose=0.2, depth=0.8)
> stc = apply_inverse(evoked, inverse_operator, lambda2,
>                     method=method, pick_ori=None)
>
> from copy import deepcopy
> noise_cov_scaled = deepcopy(noise_cov)
> noise_cov_scaled['data'] *= 1000000
> inverse_operator_scaled = make_inverse_operator(info, fwd, noise_cov_scaled,
>                                          loose=0.2, depth=0.8)
> stc_scaled = apply_inverse(evoked, inverse_operator_scaled, lambda2,
>                            method=method, pick_ori=None)
>
>
> Then stc_scaled contains exactly the same data as stc. With "dSMP" and
> "sLORETA" the result do differ. Why doesn't "MNE" care about the scale of
> the covariance matrix?
>
> Evgenii
>
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