mne.viz.plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, outlines='head', contours=6, image_interp='cubic', show=True, onselect=None, extrapolate='auto', sphere=None, border='mean', ch_type='eeg', cnorm=None)[source]#

Plot a topographic map as image.

dataarray, shape (n_chan,)

The data values to plot.

posarray, shape (n_chan, 2) | instance of Info

Location information for the data points(/channels). If an array, for each data point, the x and y coordinates. If an Info object, it must contain only one data type and exactly len(data) data channels, and the x/y coordinates will be inferred from the montage applied to the Info object.

vmin, vmaxfloat | callable() | None

Lower and upper bounds of the colormap, in the same units as the data. If vmin and vmax are both None, they are set at ± the maximum absolute value of the data (yielding a colormap with midpoint at 0). If only one of vmin, vmax is None, will use min(data) or max(data), respectively. If callable, should accept a NumPy array of data and return a float.

cmapmatplotlib colormap | None

Colormap to use. If None, ‘Reds’ is used for all positive data, otherwise defaults to ‘RdBu_r’.

sensorsbool | str

Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True (default), circles will be used.


The resolution of the topomap image (number of pixels along each side).

axesinstance of Axes | None

The axes to plot to. If None, the current axes will be used.

nameslist | None

List of channel names. If None, channel names are not plotted.

show_namesbool | callable()

If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace('MEG ', ''). If mask is not None, only significant sensors will be shown. If True, a list of names must be provided (see names keyword).

maskndarray of bool, shape (n_channels,) | None

Array indicating channel(s) to highlight with a distinct plotting style. Array elements set to True will be plotted with the parameters given in mask_params. Defaults to None, equivalent to an array of all False elements.

mask_paramsdict | None

Additional plotting parameters for plotting significant sensors. Default (None) equals:

dict(marker='o', markerfacecolor='w', markeredgecolor='k',
        linewidth=0, markersize=4)
outlines‘head’ | ‘skirt’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

contoursint | array of float

The number of contour lines to draw. If 0, no contours will be drawn. If an array, the values represent the levels for the contours. The values are in µV for EEG, fT for magnetometers and fT/m for gradiometers. Defaults to 6.


The image interpolation to be used. Options are 'cubic' (default) to use scipy.interpolate.CloughTocher2DInterpolator, 'nearest' to use scipy.spatial.Voronoi or 'linear' to use scipy.interpolate.LinearNDInterpolator.


Show figure if True.

onselectcallable() | None

Handle for a function that is called when the user selects a set of channels by rectangle selection (matplotlib RectangleSelector). If None interactive selection is disabled. Defaults to None.



  • 'box'

    Extrapolate to four points placed to form a square encompassing all data points, where each side of the square is three times the range of the data in the respective dimension.

  • 'local' (default for MEG sensors)

    Extrapolate only to nearby points (approximately to points closer than median inter-electrode distance). This will also set the mask to be polygonal based on the convex hull of the sensors.

  • 'head' (default for non-MEG sensors)

    Extrapolate out to the edges of the clipping circle. This will be on the head circle when the sensors are contained within the head circle, but it can extend beyond the head when sensors are plotted outside the head circle.

Changed in version 0.21:

  • The default was changed to 'local' for MEG sensors.

  • 'local' was changed to use a convex hull mask

  • 'head' was changed to extrapolate out to the clipping circle.

New in version 0.18.

spherefloat | array-like | instance of ConductorModel | None

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. None (the default) is equivalent to (0, 0, 0, 0.095). Currently the head radius does not affect plotting.

New in version 0.20.

borderfloat | ‘mean’

Value to extrapolate to on the topomap borders. If 'mean' (default), then each extrapolated point has the average value of its neighbours.

New in version 0.20.

ch_type‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None

The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None the first available channel type from order shown above is used. Defaults to None.

New in version 0.21.

cnormmatplotlib.colors.Normalize | None

Colormap normalization, default None means linear normalization. If not None, vmin and vmax arguments are ignored. See Notes for more details.

New in version 0.24.


The interpolated data.


The fieldlines.


The cnorm parameter can be used to implement custom colormap normalization. By default, a linear mapping from vmin to vmax is used, which correspond to the first and last colors in the colormap. This might be undesired when vmin and vmax are not symmetrical around zero (or a value that can be interpreted as some midpoint). For example, assume we want to use the RdBu colormap (red to white to blue) for values ranging from -1 to 3, and 0 should be white. However, white corresponds to the midpoint in the data by default, i.e. 1. Therefore, we use the following colormap normalization cnorm and pass it as the the cnorm argument:

from matplotlib.colors import TwoSlopeNorm
cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=3)

Note that because we define vmin and vmax in the normalization, arguments vmin and vmax to plot_topomap will be ignored if a normalization is provided. See the matplotlib docs for more details on colormap normalization.

Examples using mne.viz.plot_topomap#

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel
Plotting topographic maps of evoked data

Plotting topographic maps of evoked data

Plotting topographic maps of evoked data
Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction