mne.viz.plot_evoked_white#
- mne.viz.plot_evoked_white(evoked, noise_cov, show=True, rank=None, time_unit='s', sphere=None, axes=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| path-like The noise covariance. Can be a string to load a covariance from disk.
- showbool
Show figure if True.
- rank
None| ‘info’ | ‘full’ |dict This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).
NoneThe rank will be estimated from the data after proper scaling of different channel types.
'info'The rank is inferred from
info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.'full'The rank is assumed to be full, i.e. equal to the number of good channels. If a
Covarianceis passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.dictCalculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to channel types in the data (e.g.
'meg','mag','grad','eeg'), and whose values are integers representing the respective ranks. For example,{'mag': 90, 'eeg': 45}will assume a rank of90and45for magnetometer data and EEG data, respectively.The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
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 array-like 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 digitization-based 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.
- axes
list|None List of axes to plot into.
New in version 0.21.0.
- verbosebool |
str|int|None Control verbosity of the logging output. If
None, use the default verbosity level. See the logging documentation andmne.verbose()for details. Should only be passed as a keyword argument.
- 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 log-likelihood. 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, 328-342, NeuroImage.
