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%matplotlib inline

Plotting topographic arrowmaps of evoked data

Load evoked data and plot arrowmaps along with the topomap for selected time points. An arrowmap is based upon the Hosaka-Cohen transformation and represents an estimation of the current flow underneath the MEG sensors. They are a poor man's MNE.

See :footcite:CohenHosaka1976 for details.

References

.. footbibliography::



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# Authors: Sheraz Khan <sheraz@khansheraz.com>
#
# License: BSD (3-clause)

import numpy as np
import mne
from mne.datasets import sample
from mne.datasets.brainstorm import bst_raw
from mne import read_evokeds
from mne.viz import plot_arrowmap

print(__doc__)

path = sample.data_path()
fname = path + '/MEG/sample/sample_audvis-ave.fif'

# load evoked data
condition = 'Left Auditory'
evoked = read_evokeds(fname, condition=condition, baseline=(None, 0))
evoked_mag = evoked.copy().pick_types(meg='mag')
evoked_grad = evoked.copy().pick_types(meg='grad')

Plot magnetometer data as an arrowmap along with the topoplot at the time of the maximum sensor space activity:



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max_time_idx = np.abs(evoked_mag.data).mean(axis=0).argmax()
plot_arrowmap(evoked_mag.data[:, max_time_idx], evoked_mag.info)

# Since planar gradiometers takes gradients along latitude and longitude,
# they need to be projected to the flatten manifold span by magnetometer
# or radial gradiometers before taking the gradients in the 2D Cartesian
# coordinate system for visualization on the 2D topoplot. You can use the
# ``info_from`` and ``info_to`` parameters to interpolate from
# gradiometer data to magnetometer data.

Plot gradiometer data as an arrowmap along with the topoplot at the time of the maximum sensor space activity:



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plot_arrowmap(evoked_grad.data[:, max_time_idx], info_from=evoked_grad.info,
              info_to=evoked_mag.info)

Since Vectorview 102 system perform sparse spatial sampling of the magnetic field, data from the Vectorview (info_from) can be projected to the high density CTF 272 system (info_to) for visualization

Plot gradiometer data as an arrowmap along with the topoplot at the time of the maximum sensor space activity:



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path = bst_raw.data_path()
raw_fname = path + ('/MEG/bst_raw/'
                    'subj001_somatosensory_20111109_01_AUX-f.ds')
raw_ctf = mne.io.read_raw_ctf(raw_fname)
raw_ctf_info = mne.pick_info(
    raw_ctf.info, mne.pick_types(raw_ctf.info, meg=True, ref_meg=False))
plot_arrowmap(evoked_grad.data[:, max_time_idx], info_from=evoked_grad.info,
              info_to=raw_ctf_info, scale=6e-10)