I have been looking at how noise covariance matrices affect the inverse operator and have made a curious observation: when I compute the ncm from my continuous raw MEG data, the ncms look very different depending on whether I use â€śempiricalâ€ť or â€śshrunkâ€ť as method, but when I construct inverse operators using these very different ncms, the K matrices are virtually identical (difference < 1% for 99% of entries).

This seems quite remarkable to me: Why is the effect of the ncm so small here? Are there constellations (e.g. using epoched data, or low-quality data) where it might play a bigger role?

MNE 0.23, Python 3.9, Windows Server 2019

Code excerpt:

```
ncm_raw_sh = mne.compute_raw_covariance(data_raw, method='shrunk',
rank='info', tmin=10, tmax=60)
ncm_raw_em = mne.compute_raw_covariance(data_raw, method='empirical',
rank='info', tmin=10, tmax=60)
inv_raw_sh = mne.minimum_norm.make_inverse_operator(
data_raw.info, fwd, ncm_raw_sh, loose=0)
inv_raw_em = mne.minimum_norm.make_inverse_operator(
data_raw.info, fwd, ncm_raw_em, loose=0)
inv_prep_sh = mne.minimum_norm.prepare_inverse_operator(inv_raw_sh, 1, 1./9, 'dSPM')
inv_prep_em = mne.minimum_norm.prepare_inverse_operator(inv_raw_em, 1, 1./9, 'dSPM')
K_sh, noise_norm, vertno, source_nn = mne.minimum_norm.inverse._assemble_kernel(
inv_prep_sh, label=None, method='dSPM', pick_ori=None)
K_em, noise_norm, vertno, source_nn = mne.minimum_norm.inverse._assemble_kernel(
inv_prep_em, label=None, method='dSPM', pick_ori=None)
```