mne.viz.plot_evoked_white¶

mne.viz.
plot_evoked_white
(evoked, noise_cov, show=True, rank=None, time_unit='s', sphere=None, 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
 evokedinstance of
mne.Evoked
The evoked response.
 noise_cov
list
 instance ofCovariance
str
The noise covariance. Can be a string to load a covariance from disk.
 showbool
Show figure if True.
 rank
None
dict
 ‘info’  ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.compute_rank()
for details.The default is None. time_unit
str
The units for the time axis, can be “ms” or “s” (default).
New in version 0.16.
 sphere
float
 array_like str
None
The sphere parameters to use for the cartoon head. Can be arraylike of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give the radius (origin assumed 0, 0, 0). Can also be a spherical ConductorModel, which will use the origin and radius. Can be “auto” to use a digitizationbased fit. Can also be None (default) to use ‘auto’ when enough extra digitization points are available, and 0.095 otherwise. Currently the head radius does not affect plotting.
New in version 0.20.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 evokedinstance of
 Returns
 figinstance of
matplotlib.figure.Figure
The figure object containing the plot.
 figinstance of
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
Engemann D. and Gramfort A. (2015) Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals, vol. 108, 328342, NeuroImage.