mne.viz.plot_evoked_white

mne.viz.plot_evoked_white(evoked, noise_cov, show=True, rank=None)[source]

Plot whitened evoked response.

Plots the whitened evoked response and the whitened GFP as described in [R195195]. 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 for each noise covariance estimator that is passed.

Parameters:

evoked : instance of mne.Evoked

The evoked response.

noise_cov : list | instance of Covariance | str

The noise covariance as computed by mne.cov.compute_covariance.

show : bool

Show figure if True.

rank : dict of int | None

Dict of ints where keys are ‘eeg’, ‘meg’, mag’ or ‘grad’. If None, the rank is detected automatically. Defaults to None. ‘mag’ or ‘grad’ cannot be specified jointly with ‘meg’. For SSS’d data, only ‘meg’ is valid. For non-SSS’d data, ‘mag’ and/or ‘grad’ must be specified separately. If only one is specified, the other one gets estimated. Note. The rank estimation will be printed by the logger for each noise covariance estimator that is passed.

Returns:

fig : instance of matplotlib.figure.Figure

The figure object containing the plot.

References

[R195195](1, 2) 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.