Sensitivity map of SSP projections¶

This example shows the sources that have a forward field similar to the first SSP vector correcting for ECG.

# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

from mne import read_forward_solution, read_proj, sensitivity_map
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()

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

fwd = read_forward_solution(fname, surf_ori=True)
projs = read_proj(ecg_fname)
projs = projs[3:][::2]  # take only one projection per channel type

# Compute sensitivity map
ssp_ecg_map = sensitivity_map(fwd, ch_type='grad', projs=projs, mode='angle')

Show sensitivity map

plt.hist(ssp_ecg_map.data.ravel())
plt.show()

args = dict(clim=dict(kind='value', lims=(0.2, 0.6, 1.)), smoothing_steps=7,
            hemi='rh', subjects_dir=subjects_dir)
ssp_ecg_map.plot(subject='sample', time_label='ECG SSP sensitivity', **args)

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
colormap: fmin=2.00e-01 fmid=6.00e-01 fmax=1.00e+00 transparent=1

Total running time of the script: (0 minutes 4.128 seconds)

Download Python source code: plot_ssp_projs_sensitivity_map.py