Plotting topographic maps of evoked data

Load evoked data and plot topomaps for selected time points using multiple additional options.

# Authors: Christian Brodbeck <>
#          Tal Linzen <>
#          Denis A. Engeman <>
#          Mikołaj Magnuski <>
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 5

import numpy as np
import matplotlib.pyplot as plt
from mne.datasets import sample
from mne import read_evokeds


path = sample.data_path()
fname = path + '/MEG/sample/sample_audvis-ave.fif'

# load evoked corresponding to a specific condition
# from the fif file and subtract baseline
condition = 'Left Auditory'
evoked = read_evokeds(fname, condition=condition, baseline=(None, 0))


Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)

Basic plot_topomap options

We plot evoked topographies using mne.Evoked.plot_topomap(). The first argument, times allows to specify time instants (in seconds!) for which topographies will be shown. We select timepoints from 50 to 150 ms with a step of 20ms and plot magnetometer data:

times = np.arange(0.05, 0.151, 0.02)
evoked.plot_topomap(times, ch_type='mag', time_unit='s')

If times is set to None at most 10 regularly spaced topographies will be shown:

evoked.plot_topomap(ch_type='mag', time_unit='s')

Instead of showing topographies at specific time points we can compute averages of 50 ms bins centered on these time points to reduce the noise in the topographies:

evoked.plot_topomap(times, ch_type='mag', average=0.05, time_unit='s')

We can plot gradiometer data (plots the RMS for each pair of gradiometers)

evoked.plot_topomap(times, ch_type='grad', time_unit='s')

Additional plot_topomap options

We can also use a range of various mne.viz.plot_topomap() arguments that control how the topography is drawn. For example:

  • cmap - to specify the color map

  • res - to control the resolution of the topographies (lower resolution means faster plotting)

  • outlines='skirt' to see the topography stretched beyond the head circle

  • contours to define how many contour lines should be plotted

evoked.plot_topomap(times, ch_type='mag', cmap='Spectral_r', res=32,
                    outlines='skirt', contours=4, time_unit='s')

If you look at the edges of the head circle of a single topomap you’ll see the effect of extrapolation. By default extrapolate='box' is used which extrapolates to a large box stretching beyond the head circle. Compare this with extrapolate='head' (second topography below) where extrapolation goes to 0 at the head circle and extrapolate='local' where extrapolation is performed only within some distance from channels:

extrapolations = ['box', 'head', 'local']
fig, axes = plt.subplots(figsize=(7.5, 2.5), ncols=3)

for ax, extr in zip(axes, extrapolations):
    evoked.plot_topomap(0.1, ch_type='mag', size=2, extrapolate=extr, axes=ax,
                        show=False, colorbar=False)
    ax.set_title(extr, fontsize=14)

More advanced usage

Now we plot magnetometer data as topomap at a single time point: 100 ms post-stimulus, add channel labels, title and adjust plot margins:

evoked.plot_topomap(0.1, ch_type='mag', show_names=True, colorbar=False,
                    size=6, res=128, title='Auditory response',
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)

Animating the topomap

Instead of using a still image we can plot magnetometer data as an animation (animates only in matplotlib interactive mode)

evoked.animate_topomap(ch_type='mag', times=times, frame_rate=10,


Initializing animation...

Total running time of the script: ( 0 minutes 4.643 seconds)

Estimated memory usage: 9 MB

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