Remap MEG channel types#

In this example, MEG data are remapped from one channel type to another. This is useful to:

  • visualize combined magnetometers and gradiometers as magnetometers or gradiometers.

  • run statistics from both magnetometers and gradiometers while working with a single type of channels.

# Author: Mainak Jas <mainak.jas@telecom-paristech.fr>

# License: BSD-3-Clause
import mne
from mne.datasets import sample

print(__doc__)

# read the evoked
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
fname = meg_path / 'sample_audvis-ave.fif'
evoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0))
Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)

First, let’s call remap gradiometers to magnometers, and plot the original and remapped topomaps of the magnetometers.

# go from grad + mag to mag and plot original mag
virt_evoked = evoked.as_type('mag')
fig = evoked.plot_topomap(ch_type='mag')
fig.suptitle('mag (original)')
mag (original), -0.200 s, 0.033 s, 0.266 s, 0.499 s, fT
    Computing dot products for 305 MEG channels...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s finished
    Computing cross products for 305 → 102 MEG channels...
    Preparing the mapping matrix...
    Truncating at 210/305 components to omit less than 0.0001 (9.9e-05)
# plot interpolated grad + mag
fig = virt_evoked.plot_topomap(ch_type='mag')
fig.suptitle('mag (interpolated from mag + grad)')
mag (interpolated from mag + grad), -0.200 s, 0.033 s, 0.266 s, 0.499 s, fT

Now, we remap magnometers to gradiometers, and plot the original and remapped topomaps of the gradiometers

# go from grad + mag to grad and plot original grad
virt_evoked = evoked.as_type('grad')
fig = evoked.plot_topomap(ch_type='grad')
fig.suptitle('grad (original)')
grad (original), -0.200 s, 0.033 s, 0.266 s, 0.499 s, fT/cm
    Computing dot products for 305 MEG channels...
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s finished
    Computing cross products for 305 → 203 MEG channels...
    Preparing the mapping matrix...
    Truncating at 210/305 components to omit less than 0.0001 (9.9e-05)
# plot interpolated grad + mag
fig = virt_evoked.plot_topomap(ch_type='grad')
fig.suptitle('grad (interpolated from mag + grad)')
grad (interpolated from mag + grad), -0.200 s, 0.033 s, 0.266 s, 0.499 s, fT/cm

Total running time of the script: ( 0 minutes 12.273 seconds)

Estimated memory usage: 9 MB

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