mne.viz.plot_evoked_image(evoked, picks=None, exclude='bads', unit=True, show=True, clim=None, xlim='tight', proj=False, units=None, scalings=None, titles=None, axes=None, cmap='RdBu_r', colorbar=True, mask=None, mask_style=None, mask_cmap='Greys', mask_alpha=0.25, time_unit='s', show_names='auto', group_by=None, sphere=None)[source]#

Plot evoked data as images.

evokedinstance of Evoked

The evoked data.

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values 'all' to pick all channels, or 'data' to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided. This parameter can also be used to set the order the channels are shown in, as the channel image is sorted by the order of picks.

excludelist of str | ‘bads’

Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded.


Scale plot with channel (SI) unit.


Show figure if True.

climdict | None

Color limits for plots (after scaling has been applied). e.g. clim = dict(eeg=[-20, 20]). Valid keys are eeg, mag, grad, misc. If None, the clim parameter for each channel equals the pyplot default.

xlim‘tight’ | tuple | None

X limits for plots.

projbool | ‘interactive’

If true SSP projections are applied before display. If ‘interactive’, a check box for reversible selection of SSP projection vectors will be shown.

unitsdict | None

The units of the channel types used for axes labels. If None, defaults to dict(eeg='µV', grad='fT/cm', mag='fT').

scalingsdict | None

The scalings of the channel types to be applied for plotting. If None,` defaults to dict(eeg=1e6, grad=1e13, mag=1e15).

titlesdict | None

The titles associated with the channels. If None, defaults to dict(eeg='EEG', grad='Gradiometers', mag='Magnetometers').

axesinstance of Axes | list | dict | None

The axes to plot to. If list, the list must be a list of Axes of the same length as the number of channel types. If instance of Axes, there must be only one channel type plotted. If group_by is a dict, this cannot be a list, but it can be a dict of lists of axes, with the keys matching those of group_by. In that case, the provided axes will be used for the corresponding groups. Defaults to None.

cmapmatplotlib colormap | (colormap, bool) | ‘interactive’

Colormap. If tuple, the first value indicates the colormap to use and the second value is a boolean defining interactivity. In interactive mode the colors are adjustable by clicking and dragging the colorbar with left and right mouse button. Left mouse button moves the scale up and down and right mouse button adjusts the range. Hitting space bar resets the scale. Up and down arrows can be used to change the colormap. If ‘interactive’, translates to ('RdBu_r', True). Defaults to 'RdBu_r'.


If True, plot a colorbar. Defaults to True.

New in v0.16.

maskndarray | None

An array of booleans of the same shape as the data. Entries of the data that correspond to False in the mask are masked (see do_mask below). Useful for, e.g., masking for statistical significance.

New in v0.16.

mask_styleNone | ‘both’ | ‘contour’ | ‘mask’

If mask is not None: if ‘contour’, a contour line is drawn around the masked areas (True in mask). If ‘mask’, entries not True in mask are shown transparently. If ‘both’, both a contour and transparency are used. If None, defaults to ‘both’ if mask is not None, and is ignored otherwise.

New in v0.16.

mask_cmapmatplotlib colormap | (colormap, bool) | ‘interactive’

The colormap chosen for masked parts of the image (see below), if mask is not None. If None, cmap is reused. Defaults to Greys. Not interactive. Otherwise, as cmap.


A float between 0 and 1. If mask is not None, this sets the alpha level (degree of transparency) for the masked-out segments. I.e., if 0, masked-out segments are not visible at all. Defaults to .25.

New in v0.16.


The units for the time axis, can be “ms” or “s” (default).

New in v0.16.

show_namesbool | ‘auto’ | ‘all’

Determines if channel names should be plotted on the y axis. If False, no names are shown. If True, ticks are set automatically by matplotlib and the corresponding channel names are shown. If “all”, all channel names are shown. If “auto”, is set to False if picks is None, to True if picks contains 25 or more entries, or to “all” if picks contains fewer than 25 entries.

group_byNone | dict

If a dict, the values must be picks, and axes must also be a dict with matching keys, or None. If axes is None, one figure and one axis will be created for each entry in group_by.Then, for each entry, the picked channels will be plotted to the corresponding axis. If titles are None, keys will become plot titles. This is useful for e.g. ROIs. Each entry must contain only one channel type. For example:

group_by=dict(Left_ROI=[1, 2, 3, 4], Right_ROI=[5, 6, 7, 8])

If None, all picked channels are plotted to the same axis.

spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’

The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in v0.20.

Changed in version 1.1: Added 'eeglab' option.

figinstance of matplotlib.figure.Figure

Figure containing the images.

Examples using mne.viz.plot_evoked_image#

Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space