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 :class:~mne.Epochs objects. :depth: 2

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

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

Plotting Epochs as time series

.. sidebar:: Interactivity in pipelines and scripts

To use the interactive features of the :meth:`~mne.Epochs.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 :meth:mne.Epochs.plot method is available. It creates an interactive window where you can scroll through epochs and channels, enable/disable any unapplied :term:SSP projectors <projector> 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 :meth:~mne.Epochs.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:

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:

Just as we saw in the tut-section-raw-plot-proj section, we can plot the projectors present in an :class:~mne.Epochs object using the same :meth:~mne.Epochs.plot_projs_topomap method. Since the original three empty-room magnetometer projectors were inherited from the :class:~mne.io.Raw file, and we added two ECG projectors for each sensor type, we should see nine projector topomaps:

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

Plotting sensor locations

Just like :class:~mne.io.Raw objects, :class:~mne.Epochs objects keep track of sensor locations, which can be visualized with the :meth:~mne.Epochs.plot_sensors method:

Plotting the power spectrum of Epochs

Again, just like :class:~mne.io.Raw objects, :class:~mne.Epochs objects have a :meth:~mne.Epochs.plot_psd method for plotting the spectral density_ of the data.

It is also possible to plot spectral estimates across sensors as a scalp topography, using :meth:~mne.Epochs.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:

Just like :meth:~mne.Epochs.plot_projs_topomap, :meth:~mne.Epochs.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:

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 :meth:~mne.Epochs.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 :meth:~mne.Epochs.plot_image method; the former can be achieved with the :meth:~mne.Epochs.plot_image method (one channel at a time) or with the :meth:~mne.Epochs.plot_topo_image method (all sensors at once).

By default, the image map generated by :meth:~mne.Epochs.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. :meth:~mne.Epochs.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 :meth:~mne.Epochs.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:

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).

To plot an image map for all sensors, use :meth:~mne.Epochs.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 :meth:~mne.Epochs.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 :meth:~mne.Epochs.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 :class:~mne.channels.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.

To plot image maps for all EEG sensors, pass an EEG layout as the layout parameter of :meth:~mne.Epochs.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.

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