Note
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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
# Copyright the MNE-Python contributors.
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)")
Computing dot products for 305 MEG channels...
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)")
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)")
Computing dot products for 305 MEG channels...
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)")
Total running time of the script: (0 minutes 9.135 seconds)