Note

Click here to download the full example code

This example demonstrates the relationship between the noise covariance estimate and the MNE / dSPM source amplitudes. It computes source estimates for the SPM faces data and compares proper regularization with insufficient regularization based on the methods described in [1]. The example demonstrates that improper regularization can lead to overestimation of source amplitudes. This example makes use of the previous, non-optimized code path that was used before implementing the suggestions presented in [1].

This example does quite a bit of processing, so even on a fast machine it can take a couple of minutes to complete.

Warning

Please do not copy the patterns presented here for your own analysis, this is example is purely illustrative.

[1] | (1, 2) Engemann D. and Gramfort A. (2015) Automated model selection in
covariance estimation and spatial whitening of MEG and EEG signals,
vol. 108, 328-342, NeuroImage. |

```
# Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
print(__doc__)
```

Get data

```
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'
raw = io.read_raw_ctf(raw_fname % 1) # Take first run
# To save time and memory for this demo, we'll just use the first
# 2.5 minutes (all we need to get 30 total events) and heavily
# resample 480->60 Hz (usually you wouldn't do either of these!)
raw = raw.crop(0, 150.).load_data()
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(None, 20.)
events = mne.find_events(raw, stim_channel='UPPT001')
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.5
baseline = (None, 0)
reject = dict(mag=3e-12)
# Make forward
trans = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces1_3D_raw-trans.fif'
src = data_path + '/subjects/spm/bem/spm-oct-6-src.fif'
bem = data_path + '/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif'
forward = mne.make_forward_solution(raw.info, trans, src, bem)
del src
# inverse parameters
conditions = 'faces', 'scrambled'
snr = 3.0
lambda2 = 1.0 / snr ** 2
clim = dict(kind='value', lims=[0, 2.5, 5])
```

Out:

```
ds directory : /home/circleci/mne_data/MNE-spm-face/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds
res4 data read.
hc data read.
Separate EEG position data file not present.
Quaternion matching (desired vs. transformed):
-0.90 72.01 0.00 mm <-> -0.90 72.01 0.00 mm (orig : -43.09 61.46 -252.17 mm) diff = 0.000 mm
0.90 -72.01 0.00 mm <-> 0.90 -72.01 0.00 mm (orig : 53.49 -45.24 -258.02 mm) diff = 0.000 mm
98.30 0.00 0.00 mm <-> 98.30 -0.00 0.00 mm (orig : 78.60 72.16 -241.87 mm) diff = 0.000 mm
Coordinate transformations established.
Polhemus data for 3 HPI coils added
Device coordinate locations for 3 HPI coils added
Measurement info composed.
Finding samples for /home/circleci/mne_data/MNE-spm-face/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds/SPM_CTF_MEG_example_faces1_3D.meg4:
System clock channel is available, checking which samples are valid.
1 x 324474 = 324474 samples from 340 chs
Current compensation grade : 3
Reading 0 ... 72000 = 0.000 ... 150.000 secs...
Setting up low-pass filter at 20 Hz
h_trans_bandwidth chosen to be 5.0 Hz
Filter length of 317 samples (0.660 sec) selected
41 events found
Event IDs: [1 2 3]
Source space : /home/circleci/mne_data/MNE-spm-face/subjects/spm/bem/spm-oct-6-src.fif
MRI -> head transform : /home/circleci/mne_data/MNE-spm-face/MEG/spm/SPM_CTF_MEG_example_faces1_3D_raw-trans.fif
Measurement data : instance of Info
Conductor model : /home/circleci/mne_data/MNE-spm-face/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif
Accurate field computations
Do computations in head coordinates
Free source orientations
Reading /home/circleci/mne_data/MNE-spm-face/subjects/spm/bem/spm-oct-6-src.fif...
Read 2 source spaces a total of 8196 active source locations
Coordinate transformation: MRI (surface RAS) -> head
0.999622 0.006802 0.026647 -2.80 mm
-0.014131 0.958276 0.285497 6.72 mm
-0.023593 -0.285765 0.958009 9.43 mm
0.000000 0.000000 0.000000 1.00
Read 274 MEG channels from info
Read 29 MEG compensation channels from info
84 coil definitions read
Coordinate transformation: MEG device -> head
0.997940 -0.049681 -0.040594 -1.35 mm
0.054745 0.989330 0.135013 -0.41 mm
0.033453 -0.136957 0.990012 65.80 mm
0.000000 0.000000 0.000000 1.00
5 compensation data sets in info
MEG coil definitions created in head coordinates.
Removing 5 compensators from info because not all compensation channels were picked.
Source spaces are now in head coordinates.
Setting up the BEM model using /home/circleci/mne_data/MNE-spm-face/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif...
Loading surfaces...
Three-layer model surfaces loaded.
Loading the solution matrix...
Loaded linear_collocation BEM solution from /home/circleci/mne_data/MNE-spm-face/subjects/spm/bem/spm-5120-5120-5120-bem-sol.fif
Employing the head->MRI coordinate transform with the BEM model.
BEM model spm-5120-5120-5120-bem-sol.fif is now set up
Source spaces are in head coordinates.
Checking that the sources are inside the bounding surface (will take a few...)
Thank you for waiting.
Setting up compensation data...
274 out of 274 channels have the compensation set.
Desired compensation data (3) found.
All compensation channels found.
Preselector created.
Compensation data matrix created.
Postselector created.
Composing the field computation matrix...
Composing the field computation matrix (compensation coils)...
Computing MEG at 8196 source locations (free orientations)...
Finished.
```

Estimate covariances

```
samples_epochs = 5, 15,
method = 'empirical', 'shrunk'
colors = 'steelblue', 'red'
evokeds = list()
stcs = list()
methods_ordered = list()
for n_train in samples_epochs:
# estimate covs based on a subset of samples
# make sure we have the same number of conditions.
events_ = np.concatenate([events[events[:, 2] == id_][:n_train]
for id_ in [event_ids[k] for k in conditions]])
events_ = events_[np.argsort(events_[:, 0])]
epochs_train = mne.Epochs(raw, events_, event_ids, tmin, tmax, picks=picks,
baseline=baseline, preload=True, reject=reject,
decim=8)
epochs_train.equalize_event_counts(event_ids)
assert len(epochs_train) == 2 * n_train
# We know some of these have too few samples, so suppress warning
# with verbose='error'
noise_covs = compute_covariance(
epochs_train, method=method, tmin=None, tmax=0, # baseline only
return_estimators=True, rank=None, verbose='error') # returns list
# prepare contrast
evokeds = [epochs_train[k].average() for k in conditions]
del epochs_train, events_
# do contrast
# We skip empirical rank estimation that we introduced in response to
# the findings in reference [1] to use the naive code path that
# triggered the behavior described in [1]. The expected true rank is
# 274 for this dataset. Please do not do this with your data but
# rely on the default rank estimator that helps regularizing the
# covariance.
stcs.append(list())
methods_ordered.append(list())
for cov in noise_covs:
inverse_operator = make_inverse_operator(evokeds[0].info, forward,
cov, loose=0.2, depth=0.8,
rank=274)
stc_a, stc_b = (apply_inverse(e, inverse_operator, lambda2, "dSPM",
pick_ori=None) for e in evokeds)
stc = stc_a - stc_b
methods_ordered[-1].append(cov['method'])
stcs[-1].append(stc)
del inverse_operator, evokeds, cov, noise_covs, stc, stc_a, stc_b
del raw, forward # save some memory
```

Out:

```
10 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 10 events and 337 original time points ...
0 bad epochs dropped
Dropped 0 epochs
Converting forward solution to surface orientation
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
Computing inverse operator with 274 channels.
Removing 5 compensators from info because not all compensation channels were picked.
estimated rank (mag): 130
Setting small MAG eigenvalues to zero.
Not doing PCA for MAG.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Removing 5 compensators from info because not all compensation channels were picked.
Creating the depth weighting matrix...
274 magnetometer or axial gradiometer channels
limit = 8109/8196 = 10.042069
scale = 4.04483e-11 exp = 0.8
Computing inverse operator with 274 channels.
Creating the source covariance matrix
Applying loose dipole orientations. Loose value of 0.2.
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 3.92539
scaling factor to adjust the trace = 2.3235e+19
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 5
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "faces"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 91.8% variance
Combining the current components...
dSPM...
[done]
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 5
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "scrambled"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 91.7% variance
Combining the current components...
dSPM...
[done]
Converting forward solution to surface orientation
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
Computing inverse operator with 274 channels.
Removing 5 compensators from info because not all compensation channels were picked.
estimated rank (mag): 130
Setting small MAG eigenvalues to zero.
Not doing PCA for MAG.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Removing 5 compensators from info because not all compensation channels were picked.
Creating the depth weighting matrix...
274 magnetometer or axial gradiometer channels
limit = 8109/8196 = 10.042069
scale = 4.04483e-11 exp = 0.8
Computing inverse operator with 274 channels.
Creating the source covariance matrix
Applying loose dipole orientations. Loose value of 0.2.
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 5.07378
scaling factor to adjust the trace = 2.1613e+23
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 5
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "faces"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 99.7% variance
Combining the current components...
dSPM...
[done]
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 5
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 130 (144 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "scrambled"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 99.6% variance
Combining the current components...
dSPM...
[done]
30 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
0 projection items activated
Loading data for 30 events and 337 original time points ...
0 bad epochs dropped
Dropped 0 epochs
Converting forward solution to surface orientation
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
Computing inverse operator with 274 channels.
Removing 5 compensators from info because not all compensation channels were picked.
estimated rank (mag): 274
Setting small MAG eigenvalues to zero.
Not doing PCA for MAG.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Removing 5 compensators from info because not all compensation channels were picked.
Creating the depth weighting matrix...
274 magnetometer or axial gradiometer channels
limit = 8109/8196 = 10.042069
scale = 4.04483e-11 exp = 0.8
Computing inverse operator with 274 channels.
Creating the source covariance matrix
Applying loose dipole orientations. Loose value of 0.2.
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 4.12203
scaling factor to adjust the trace = 2.35095e+19
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 15
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "faces"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 86.6% variance
Combining the current components...
dSPM...
[done]
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 15
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "scrambled"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 86.9% variance
Combining the current components...
dSPM...
[done]
Converting forward solution to surface orientation
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
Computing inverse operator with 274 channels.
Removing 5 compensators from info because not all compensation channels were picked.
estimated rank (mag): 274
Setting small MAG eigenvalues to zero.
Not doing PCA for MAG.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Removing 5 compensators from info because not all compensation channels were picked.
Creating the depth weighting matrix...
274 magnetometer or axial gradiometer channels
limit = 8109/8196 = 10.042069
scale = 4.04483e-11 exp = 0.8
Computing inverse operator with 274 channels.
Creating the source covariance matrix
Applying loose dipole orientations. Loose value of 0.2.
Whitening the forward solution.
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 5.41039
scaling factor to adjust the trace = 2.16434e+20
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 15
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "faces"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 93.1% variance
Combining the current components...
dSPM...
[done]
Removing 5 compensators from info because not all compensation channels were picked.
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 15
Created the regularized inverter
The projection vectors do not apply to these channels.
Created the whitener using a noise covariance matrix with rank 274 (0 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to "scrambled"...
Picked 274 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Computing residual...
Explained 93.2% variance
Combining the current components...
dSPM...
[done]
```

Show the resulting source estimates

```
fig, (axes1, axes2) = plt.subplots(2, 3, figsize=(9.5, 5))
for ni, (n_train, axes) in enumerate(zip(samples_epochs, (axes1, axes2))):
# compute stc based on worst and best
ax_dynamics = axes[1]
for stc, ax, method, kind, color in zip(stcs[ni],
axes[::2],
methods_ordered[ni],
['best', 'worst'],
colors):
brain = stc.plot(subjects_dir=subjects_dir, hemi='both', clim=clim,
initial_time=0.175, background='w', foreground='k')
brain.show_view('ven')
im = brain.screenshot()
brain.close()
ax.axis('off')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.imshow(im)
ax.set_title('{0} ({1} epochs)'.format(kind, n_train * 2))
# plot spatial mean
stc_mean = stc.data.mean(0)
ax_dynamics.plot(stc.times * 1e3, stc_mean,
label='{0} ({1})'.format(method, kind),
color=color)
# plot spatial std
stc_var = stc.data.std(0)
ax_dynamics.fill_between(stc.times * 1e3, stc_mean - stc_var,
stc_mean + stc_var, alpha=0.2, color=color)
# signal dynamics worst and best
ax_dynamics.set(title='{0} epochs'.format(n_train * 2),
xlabel='Time (ms)', ylabel='Source Activation (dSPM)',
xlim=(tmin * 1e3, tmax * 1e3), ylim=(-3, 3))
ax_dynamics.legend(loc='upper left', fontsize=10)
fig.subplots_adjust(hspace=0.2, left=0.01, right=0.99, wspace=0.03)
```

**Total running time of the script:** ( 3 minutes 26.973 seconds)

**Estimated memory usage:** 1257 MB