This estimates the SNR as a function of time for a set of data.
Script output:
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 47
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a full noise covariance matrix (3 small eigenvalues omitted)
Picked 305 channels from the data
Effective nchan = 305 - 3 = 302
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
from mne.datasets.sample import data_path
from mne.minimum_norm import read_inverse_operator
from mne import read_evokeds
from mne.viz import plot_snr_estimate
print(__doc__)
data_dir = op.join(data_path(), 'MEG', 'sample')
fname_inv = op.join(data_dir, 'sample_audvis-meg-oct-6-meg-inv.fif')
fname_evoked = op.join(data_dir, 'sample_audvis-ave.fif')
inv = read_inverse_operator(fname_inv)
evoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]
plot_snr_estimate(evoked, inv)
Total running time of the script: (0 minutes 0.642 seconds)
Download Python source code: plot_snr_estimate.py