Visualizing epoched data

This tutorial shows how to plot epoched data as time series, how to plot the spectral density of epoched data, how to plot epochs as an imagemap, and how to plot the sensor locations and projectors stored in Epochs objects.

We’ll start by importing the modules we need, loading the continuous (raw) sample data, and cropping it to save memory:

import os
import mne

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                                    'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120)

To create the Epochs data structure, we’ll extract the event IDs stored in the stim channel, map those integer event IDs to more descriptive condition labels using an event dictionary, and pass those to the Epochs constructor, along with the Raw data and the desired temporal limits of our epochs, tmin and tmax (for a detailed explanation of these steps, see The Epochs data structure: discontinuous data).

events = mne.find_events(raw, stim_channel='STI 014')
event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
              'visual/right': 4, 'face': 5, 'buttonpress': 32}
epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.5, event_id=event_dict,
                    preload=True)
del raw

Out:

176 events found
Event IDs: [ 1  2  3  4  5 32]
Not setting metadata
Not setting metadata
176 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 176 events and 421 original time points ...
1 bad epochs dropped

Plotting Epochs as time series

Interactivity in pipelines and scripts

To use the interactive features of the plot() method when running your code non-interactively, pass the block=True parameter, which halts the Python interpreter until the figure window is closed. That way, any channels or epochs that you mark as “bad” will be taken into account in subsequent processing steps.

To visualize epoched data as time series (one time series per channel), the mne.Epochs.plot() method is available. It creates an interactive window where you can scroll through epochs and channels, enable/disable any unapplied SSP projectors to see how they affect the signal, and even manually mark bad channels (by clicking the channel name) or bad epochs (by clicking the data) for later dropping. Channels marked “bad” will be shown in light grey color and will be added to epochs.info['bads']; epochs marked as bad will be indicated as 'USER' in epochs.drop_log.

Here we’ll plot only the “catch” trials from the sample dataset, and pass in our events array so that the button press responses also get marked (we’ll plot them in red, and plot the “face” events defining time zero for each epoch in blue). We also need to pass in our event_dict so that the plot() method will know what we mean by “buttonpress” — this is because subsetting the conditions by calling epochs['face'] automatically purges the dropped entries from epochs.event_id:

catch_trials_and_buttonpresses = mne.pick_events(events, include=[5, 32])
epochs['face'].plot(events=catch_trials_and_buttonpresses, event_id=event_dict,
                    event_colors=dict(buttonpress='red', face='blue'))

Plotting projectors from an Epochs object

In the plot above we can see heartbeat artifacts in the magnetometer channels, so before we continue let’s load ECG projectors from disk and apply them to the data:

ecg_proj_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                             'sample_audvis_ecg-proj.fif')
ecg_projs = mne.read_proj(ecg_proj_file)
epochs.add_proj(ecg_projs)
epochs.apply_proj()

Out:

    Read a total of 6 projection items:
        ECG-planar-999--0.200-0.400-PCA-01 (1 x 203)  idle
        ECG-planar-999--0.200-0.400-PCA-02 (1 x 203)  idle
        ECG-axial-999--0.200-0.400-PCA-01 (1 x 102)  idle
        ECG-axial-999--0.200-0.400-PCA-02 (1 x 102)  idle
        ECG-eeg-999--0.200-0.400-PCA-01 (1 x 59)  idle
        ECG-eeg-999--0.200-0.400-PCA-02 (1 x 59)  idle
6 projection items deactivated
Created an SSP operator (subspace dimension = 9)
9 projection items activated
SSP projectors applied...

Just as we saw in the Plotting projectors from Raw objects section, we can plot the projectors present in an Epochs object using the same plot_projs_topomap() method. Since the original three empty-room magnetometer projectors were inherited from the Raw file, and we added two ECG projectors for each sensor type, we should see nine projector topomaps:

epochs.plot_projs_topomap(vlim='joint')

Note that these field maps illustrate aspects of the signal that have already been removed (because projectors in Raw data are applied by default when epoching, and because we called apply_proj() after adding additional ECG projectors from file). You can check this by examining the 'active' field of the projectors:

print(all(proj['active'] for proj in epochs.info['projs']))

Out:

True

Plotting sensor locations

Just like Raw objects, Epochs objects keep track of sensor locations, which can be visualized with the plot_sensors() method:

epochs.plot_sensors(kind='3d', ch_type='all')
epochs.plot_sensors(kind='topomap', ch_type='all')

• 
• 

Plotting the power spectrum of Epochs

Again, just like Raw objects, Epochs objects have a plot_psd() method for plotting the spectral density of the data.

epochs['auditory'].plot_psd(picks='eeg')

Out:

Using multitaper spectrum estimation with 7 DPSS windows

It is also possible to plot spectral estimates across sensors as a scalp topography, using plot_psd_topomap(). The default parameters will plot five frequency bands (δ, θ, α, β, γ), will compute power based on magnetometer channels, and will plot the power estimates in decibels:

epochs['visual/right'].plot_psd_topomap()

Out:

Using multitaper spectrum estimation with 7 DPSS windows

Just like plot_projs_topomap(), plot_psd_topomap() has a vlim='joint' option for fixing the colorbar limits jointly across all subplots, to give a better sense of the relative magnitude in each band. You can change which channel type is used via the ch_type parameter, and if you want to view different frequency bands than the defaults, the bands parameter takes a list of tuples, with each tuple containing either a single frequency and a subplot title, or lower/upper frequency limits and a subplot title:

bands = [(10, '10 Hz'), (15, '15 Hz'), (20, '20 Hz'), (10, 20, '10-20 Hz')]
epochs['visual/right'].plot_psd_topomap(bands=bands, vlim='joint',
                                        ch_type='grad')

Out:

Using multitaper spectrum estimation with 7 DPSS windows

If you prefer untransformed power estimates, you can pass dB=False. It is also possible to normalize the power estimates by dividing by the total power across all frequencies, by passing normalize=True. See the docstring of plot_psd_topomap() for details.

Plotting Epochs as an image map

A convenient way to visualize many epochs simultaneously is to plot them as an image map, with each row of pixels in the image representing a single epoch, the horizontal axis representing time, and each pixel’s color representing the signal value at that time sample for that epoch. Of course, this requires either a separate image map for each channel, or some way of combining information across channels. The latter is possible using the plot_image() method; the former can be achieved with the plot_image() method (one channel at a time) or with the plot_topo_image() method (all sensors at once).

By default, the image map generated by plot_image() will be accompanied by a scalebar indicating the range of the colormap, and a time series showing the average signal across epochs and a bootstrapped 95% confidence band around the mean. plot_image() is a highly customizable method with many parameters, including customization of the auxiliary colorbar and averaged time series subplots. See the docstrings of plot_image() and mne.viz.plot_compare_evokeds (which is used to plot the average time series) for full details. Here we’ll show the mean across magnetometers for all epochs with an auditory stimulus:

epochs['auditory'].plot_image(picks='mag', combine='mean')

Out:

Not setting metadata
Not setting metadata
81 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
combining channels using "mean"

To plot image maps for individual sensors or a small group of sensors, use the picks parameter. Passing combine=None (the default) will yield separate plots for each sensor in picks; passing combine='gfp' will plot the global field power (useful for combining sensors that respond with opposite polarity).

epochs['auditory'].plot_image(picks=['MEG 0242', 'MEG 0243'])
epochs['auditory'].plot_image(picks=['MEG 0242', 'MEG 0243'], combine='gfp')

• 
• 
• 

Out:

Not setting metadata
Not setting metadata
81 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
Not setting metadata
Not setting metadata
81 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
Not setting metadata
Not setting metadata
81 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
combining channels using "gfp"

To plot an image map for all sensors, use plot_topo_image(), which is optimized for plotting a large number of image maps simultaneously, and (in interactive sessions) allows you to click on each small image map to pop open a separate figure with the full-sized image plot (as if you had called plot_image() on just that sensor). At the small scale shown in this tutorial it’s hard to see much useful detail in these plots; it’s often best when plotting interactively to maximize the topo image plots to fullscreen. The default is a figure with black background, so here we specify a white background and black foreground text. By default plot_topo_image() will show magnetometers and gradiometers on the same plot (and hence not show a colorbar, since the sensors are on different scales) so we’ll also pass a Layout restricting each plot to one channel type. First, however, we’ll also drop any epochs that have unusually high signal levels, because they can cause the colormap limits to be too extreme and therefore mask smaller signal fluctuations of interest.

reject_criteria = dict(mag=3000e-15,     # 3000 fT
                       grad=3000e-13,    # 3000 fT/cm
                       eeg=150e-6)       # 150 µV
epochs.drop_bad(reject=reject_criteria)

for ch_type, title in dict(mag='Magnetometers', grad='Gradiometers').items():
    layout = mne.channels.find_layout(epochs.info, ch_type=ch_type)
    epochs['auditory/left'].plot_topo_image(layout=layout, fig_facecolor='w',
                                            font_color='k', title=title)

• 
• 

Out:

    Rejecting  epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 015', 'EEG 016', 'EEG 023', 'EEG 039', 'EEG 041', 'EEG 044', 'EEG 045', 'EEG 046', 'EEG 047', 'EEG 048', 'EEG 049', 'EEG 050', 'EEG 051', 'EEG 052', 'EEG 054', 'EEG 055', 'EEG 056', 'EEG 057', 'EEG 058', 'EEG 059']
    Rejecting  epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 007', 'EEG 048', 'EEG 055']
    Rejecting  epoch based on EEG : ['EEG 007']
    Rejecting  epoch based on EEG : ['EEG 003', 'EEG 007']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 007']
    Rejecting  epoch based on EEG : ['EEG 001', 'EEG 002', 'EEG 007']
    Rejecting  epoch based on MAG : ['MEG 1711']
8 bad epochs dropped
Removing projector <Projection | ECG-planar-999--0.200-0.400-PCA-01, active : True, n_channels : 203>
Removing projector <Projection | ECG-planar-999--0.200-0.400-PCA-02, active : True, n_channels : 203>
Removing projector <Projection | ECG-eeg-999--0.200-0.400-PCA-01, active : True, n_channels : 59>
Removing projector <Projection | ECG-eeg-999--0.200-0.400-PCA-02, active : True, n_channels : 59>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | ECG-axial-999--0.200-0.400-PCA-01, active : True, n_channels : 102>
Removing projector <Projection | ECG-axial-999--0.200-0.400-PCA-02, active : True, n_channels : 102>
Removing projector <Projection | ECG-eeg-999--0.200-0.400-PCA-01, active : True, n_channels : 59>
Removing projector <Projection | ECG-eeg-999--0.200-0.400-PCA-02, active : True, n_channels : 59>

To plot image maps for all EEG sensors, pass an EEG layout as the layout parameter of plot_topo_image(). Note also here the use of the sigma parameter, which smooths each image map along the vertical dimension (across epochs) which can make it easier to see patterns across the small image maps (by smearing noisy epochs onto their neighbors, while reinforcing parts of the image where adjacent epochs are similar). However, sigma can also disguise epochs that have persistent extreme values and maybe should have been excluded, so it should be used with caution.

layout = mne.channels.find_layout(epochs.info, ch_type='eeg')
epochs['auditory/left'].plot_topo_image(layout=layout, fig_facecolor='w',
                                        font_color='k', sigma=1)

Out:

Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | ECG-planar-999--0.200-0.400-PCA-01, active : True, n_channels : 203>
Removing projector <Projection | ECG-planar-999--0.200-0.400-PCA-02, active : True, n_channels : 203>
Removing projector <Projection | ECG-axial-999--0.200-0.400-PCA-01, active : True, n_channels : 102>
Removing projector <Projection | ECG-axial-999--0.200-0.400-PCA-02, active : True, n_channels : 102>

Estimated memory usage: 410 MB