Read a forward operator and display sensitivity mapsΒΆ

Forward solutions can be read using read_forward_solution in Python.

# Author: Alexandre Gramfort <>
#         Denis Engemann <>
# License: BSD (3-clause)

import mne
from mne.datasets import sample
import matplotlib.pyplot as plt


data_path = sample.data_path()

fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
subjects_dir = data_path + '/subjects'

fwd = mne.read_forward_solution(fname, surf_ori=True)
leadfield = fwd['sol']['data']

print("Leadfield size : %d x %d" % leadfield.shape)

Script output:

Leadfield size : 366 x 22494

Show gain matrix a.k.a. leadfield matrix with sensitivity map

picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)
picks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)

fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
fig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)

for ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):
    im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',
    plt.colorbar(im, ax=ax, cmap='RdBu_r')

Show sensitivity of each sensor type to dipoles in the source space

grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')
mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')

         bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],
         color=['c', 'b', 'k'])
plt.title('Normal orientation sensitivity')

# Cautious smoothing to see actual dipoles
grad_map.plot(time_label='Gradiometer sensitivity', subjects_dir=subjects_dir,
              clim=dict(lims=[0, 50, 100]))

# Note. The source space uses min-dist and therefore discards most
# superficial dipoles. This is why parts of the gyri are not covered.
  • ../../_images/sphx_glr_plot_read_forward_002.png
  • ../../_images/sphx_glr_plot_read_forward_001.png

Script output:

Updating smoothing matrix, be patient..
Smoothing matrix creation, step 1
Smoothing matrix creation, step 2
Smoothing matrix creation, step 3
Smoothing matrix creation, step 4
Smoothing matrix creation, step 5
Smoothing matrix creation, step 6
Smoothing matrix creation, step 7
Smoothing matrix creation, step 8
Smoothing matrix creation, step 9
Smoothing matrix creation, step 10
colormap: fmin=2.11e-02 fmid=3.22e-01 fmax=1.00e+00 transparent=1

Total running time of the script: (0 minutes 7.732 seconds)

Download Python source code: