Plotting topographic maps of evoked data

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

# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
#          Tal Linzen <linzen@nyu.edu>
#          Denis A. Engeman <denis.engemann@gmail.com>
#          Mikołaj Magnuski <mmagnuski@swps.edu.pl>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

from mne.datasets import sample
from mne import read_evokeds

print(__doc__)

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))

Out:

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')
0.050 s, 0.070 s, 0.090 s, 0.110 s, 0.130 s, 0.150 s, fT

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

evoked.plot_topomap(ch_type='mag', time_unit='s')
-0.200 s, 0.033 s, 0.266 s, 0.499 s, fT

We can use nrows and ncols parameter to create multiline plots with more timepoints.

all_times = np.arange(-0.2, 0.5, 0.03)
evoked.plot_topomap(all_times, ch_type='mag', time_unit='s',
                    ncols=8, nrows='auto')
-0.200 s, -0.170 s, -0.140 s, -0.110 s, -0.080 s, -0.050 s, -0.020 s, 0.010 s, 0.040 s, 0.070 s, 0.100 s, 0.130 s, 0.160 s, 0.190 s, 0.220 s, 0.250 s, 0.280 s, 0.310 s, 0.340 s, 0.370 s, 0.400 s, 0.430 s, 0.460 s, 0.490 s, fT

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')
0.050 s, 0.070 s, 0.090 s, 0.110 s, 0.130 s, 0.150 s, fT

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

evoked.plot_topomap(times, ch_type='grad', time_unit='s')
0.050 s, 0.070 s, 0.090 s, 0.110 s, 0.130 s, 0.150 s, fT/cm

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')
0.050 s, 0.070 s, 0.090 s, 0.110 s, 0.130 s, 0.150 s, fT

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)

# Here we look at EEG channels, and use a custom head sphere to get all the
# sensors to be well within the drawn head surface
for ax, extr in zip(axes, extrapolations):
    evoked.plot_topomap(0.1, ch_type='eeg', size=2, extrapolate=extr, axes=ax,
                        show=False, colorbar=False, sphere=(0., 0., 0., 0.09))
    ax.set_title(extr, fontsize=14)
box, head, local

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',
                    time_unit='s', extrapolate='local', border='mean')
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88)
Auditory response, 0.100 s

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,
                       time_unit='s')
fT

Out:

Initializing animation...

Total running time of the script: ( 0 minutes 8.636 seconds)

Estimated memory usage: 10 MB

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