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.
importmne frommne.datasetsimport 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 7.857 seconds)