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 [1] for details.

References

[1]

David Cohen and Hidehiro Hosaka. Part II magnetic field produced by a current dipole. Journal of Electrocardiology, 9(4):409–417, 1976. doi:10.1016/S0022-0736(76)80041-6.

# Authors: Sheraz Khan <sheraz@khansheraz.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne import read_evokeds
from mne.datasets import sample
from mne.datasets.brainstorm import bst_raw
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(picks="mag", exclude="bads")
evoked_grad = evoked.copy().pick(picks="grad", exclude="bads")

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

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:

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:

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,
)

Estimated memory usage: 0 MB