Estimate covariance matrix from a raw FIF fileΒΆ

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

import mne
from mne import io
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw.fif'

raw = io.Raw(fname)

include = []  # or stim channels ['STI 014']
raw.info['bads'] += ['EEG 053']  # bads + 1 more

# pick EEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=False, eog=True,
                       include=include, exclude='bads')
# setup rejection
reject = dict(eeg=80e-6, eog=150e-6)

# Compute the covariance from the raw data
cov = mne.compute_raw_covariance(raw, picks=picks, reject=reject)
print(cov)

Script output:

<Covariance  |  size : 365 x 365, n_samples : 128926, data : [[  5.26618768e-23   1.66242502e-23  -5.43032308e-25 ...,   3.87037416e-17
    4.08735490e-17  -8.98324794e-17]
 [  1.66242502e-23   3.51083438e-23  -8.45087524e-25 ...,   4.93144457e-17
    4.43234560e-17  -1.77341755e-16]
 [ -5.43032308e-25  -8.45087524e-25   3.16802808e-25 ...,  -4.42192297e-18
   -3.34979293e-18   1.23631514e-17]
 ...,
 [  3.87037416e-17   4.93144457e-17  -4.42192297e-18 ...,   3.10506716e-10
    2.47580672e-10  -5.10994239e-10]
 [  4.08735490e-17   4.43234560e-17  -3.34979293e-18 ...,   2.47580672e-10
    2.34878454e-10  -4.16406684e-10]
 [ -8.98324794e-17  -1.77341755e-16   1.23631514e-17 ...,  -5.10994239e-10
   -4.16406684e-10   1.91962239e-09]]>

Show covariance

fig_cov, fig_svd = mne.viz.plot_cov(cov, raw.info, colorbar=True, proj=True)
# try setting proj to False to see the effect
  • ../../_images/sphx_glr_plot_estimate_covariance_matrix_raw_001.png
  • ../../_images/sphx_glr_plot_estimate_covariance_matrix_raw_002.png

Total running time of the script: (0 minutes 7.460 seconds)

Download Python source code: plot_estimate_covariance_matrix_raw.py