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_covlist | instance of Covariance | str: The noise covariance. Can be a string to load a covariance from disk.
showbool: Show figure if True.
rankNone | dict | ‘info’ | ‘full’: This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details.The default is None.
time_unitstr: The units for the time axis, can be “ms” or “s” (default).

New in version 0.16.
spherefloat | 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.
axeslist | None: List of axes to plot into.

New in version 0.21.0.
verbosebool, str, int, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

figinstance of matplotlib.figure.Figure: The figure object containing the plot.

See also

mne.Evoked.plot

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.