mne.viz.
plot_evoked_white
(evoked, noise_cov, show=True, rank=None, time_unit='s', verbose=None)[source]¶Plot whitened evoked response.
Plots the whitened evoked response and the whitened GFP as described in [1]. This function is especially useful for investigating noise covariance properties to determine if data are properly whitened (e.g., achieving expected values in line with model assumptions, see Notes below).
Parameters: 


Returns: 

See also
Notes
If baseline signals match the assumption of Gaussian white noise, values should be centered at 0, and be within 2 standard deviations (±1.96) for 95% of the time points. For the global field power (GFP), we expect it to fluctuate around a value of 1.
If one single covariance object is passed, the GFP panel (bottom)
will depict different sensor types. If multiple covariance objects are
passed as a list, the left column will display the whitened evoked
responses for each channel based on the whitener from the noise covariance
that has the highest loglikelihood. The left column will depict the
whitened GFPs based on each estimator separately for each sensor type.
Instead of numbers of channels the GFP display shows the estimated rank.
Note. The rank estimation will be printed by the logger
(if verbose=True
) for each noise covariance estimator that is passed.
References
[1]  (1, 2) Engemann D. and Gramfort A. (2015) Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, vol. 108, 328342, NeuroImage. 
mne.viz.plot_evoked_white
¶