Note
Click here to download the full example code
This tutorial demonstrates how to plot whitened evoked data.
Data are whitened for many processes, including dipole fitting, source localization and some decoding algorithms. Viewing whitened data thus gives a different perspective on the data that these algorithms operate on.
Let’s start by loading some data and computing a signal (spatial) covariance that we’ll consider to be noise.
import mne
from mne.datasets import sample
Note
In the mne.io.Raw.plot() with noise_cov supplied,
you can press they “w” key to turn whitening on and off.
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
events = mne.find_events(raw, stim_channel='STI 014')
event_id = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
'visual/right': 4, 'smiley': 5, 'button': 32}
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id=event_id, reject=reject)
# baseline noise cov, not a lot of samples
noise_cov = mne.compute_covariance(epochs, tmax=0., method='shrunk',
verbose='error')
# butterfly mode shows the differences most clearly
raw.plot(events=events, butterfly=True)
raw.plot(noise_cov=noise_cov, events=events, butterfly=True)
evoked = epochs.average()
evoked.plot(time_unit='s')
evoked.plot(noise_cov=noise_cov, time_unit='s')
The mne.Evoked.plot_white() function takes an additional step of
scaling the whitened plots to show how well the assumption of Gaussian
noise is satisfied by the data:
evoked.plot_white(noise_cov=noise_cov, time_unit='s')
evoked.comment = 'All trials'
evoked.plot_topo(title='Evoked data')
evoked.plot_topo(noise_cov=noise_cov, title='Whitened evoked data')
Total running time of the script: ( 0 minutes 36.208 seconds)