The Evoked data structure: evoked/averaged data

This tutorial covers the basics of creating and working with :term:evoked data. It introduces the :class:~mne.Evoked data structure in detail, including how to load, query, subselect, export, and plot data from an :class:~mne.Evoked object. For info on creating an :class:~mne.Evoked object from (possibly simulated) data in a :class:`NumPy array

<numpy.ndarray>, seetut_creating_data_structures`. :depth: 2

As usual we'll start by importing the modules we need:

Creating Evoked objects from Epochs

:class:~mne.Evoked objects typically store an EEG or MEG signal that has been averaged over multiple :term:epochs, which is a common technique for estimating stimulus-evoked activity. The data in an :class:~mne.Evoked object are stored in an :class:array <numpy.ndarray> of shape (n_channels, n_times) (in contrast to an :class:~mne.Epochs object, which stores data of shape (n_epochs, n_channels, n_times)). Thus to create an :class:~mne.Evoked object, we'll start by epoching some raw data, and then averaging together all the epochs from one condition:

Basic visualization of Evoked objects

We can visualize the average evoked response for left-auditory stimuli using the :meth:~mne.Evoked.plot method, which yields a butterfly plot of each channel type:

Like the plot() methods for :meth:Raw <mne.io.Raw.plot> and :meth:Epochs <mne.Epochs.plot> objects, :meth:evoked.plot() <mne.Evoked.plot> has many parameters for customizing the plot output, such as color-coding channel traces by scalp location, or plotting the :term:global field power <GFP> alongside the channel traces. See tut-visualize-evoked for more information about visualizing :class:~mne.Evoked objects.

Subselecting Evoked data

.. sidebar:: Evokeds are not memory-mapped

:class:~mne.Evoked objects use a :attr:~mne.Evoked.data attribute rather than a :meth:~mne.Epochs.get_data method; this reflects the fact that the data in :class:~mne.Evoked objects are always loaded into memory, never memory-mapped_ from their location on disk (because they are typically much smaller than :class:~mne.io.Raw or :class:~mne.Epochs objects).

Unlike :class:~mne.io.Raw and :class:~mne.Epochs objects, :class:~mne.Evoked objects do not support selection by square-bracket indexing. Instead, data can be subselected by indexing the :attr:~mne.Evoked.data attribute:

To select based on time in seconds, the :meth:~mne.Evoked.time_as_index method can be useful, although beware that depending on the sampling frequency, the number of samples in a span of given duration may not always be the same (see the time-as-index section of the tutorial about Raw data <tut-raw-class> for details).

Selecting, dropping, and reordering channels

By default, when creating :class:~mne.Evoked data from an :class:~mne.Epochs object, only the "data" channels will be retained: eog, ecg, stim, and misc channel types will be dropped. You can control which channel types are retained via the picks parameter of :meth:epochs.average() <mne.Epochs.average>, by passing 'all' to retain all channels, or by passing a list of integers, channel names, or channel types. See the documentation of :meth:~mne.Epochs.average for details.

If you've already created the :class:~mne.Evoked object, you can use the :meth:~mne.Evoked.pick, :meth:~mne.Evoked.pick_channels, :meth:~mne.Evoked.pick_types, and :meth:~mne.Evoked.drop_channels methods to modify which channels are included in an :class:~mne.Evoked object. You can also use :meth:~mne.Evoked.reorder_channels for this purpose; any channel names not provided to :meth:~mne.Evoked.reorder_channels will be dropped. Note that channel selection methods modify the object in-place, so in interactive/exploratory sessions you may want to create a :meth:~mne.Evoked.copy first.

Similarities among the core data structures

:class:~mne.Evoked objects have many similarities with :class:~mne.io.Raw and :class:~mne.Epochs objects, including:

• They can be loaded from and saved to disk in .fif format, and their data can be exported to a :class:NumPy array <numpy.ndarray> (but through the :attr:~mne.Evoked.data attribute, not through a get_data() method). :class:Pandas DataFrame <pandas.DataFrame> export is also available through the :meth:~mne.Evoked.to_data_frame method.

• You can change the name or type of a channel using :meth:evoked.rename_channels() <mne.Evoked.rename_channels> or :meth:evoked.set_channel_types() <mne.Evoked.set_channel_types>. Both methods take :class:dictionaries <dict> where the keys are existing channel names, and the values are the new name (or type) for that channel. Existing channels that are not in the dictionary will be unchanged.

• :term:SSP projector <projector> manipulation is possible through :meth:~mne.Evoked.add_proj, :meth:~mne.Evoked.del_proj, and :meth:~mne.Evoked.plot_projs_topomap methods, and the :attr:~mne.Evoked.proj attribute. See tut-artifact-ssp for more information on SSP.

• Like :class:~mne.io.Raw and :class:~mne.Epochs objects, :class:~mne.Evoked objects have :meth:~mne.Evoked.copy, :meth:~mne.Evoked.crop, :meth:~mne.Evoked.time_as_index, :meth:~mne.Evoked.filter, and :meth:~mne.Evoked.resample methods.

• Like :class:~mne.io.Raw and :class:~mne.Epochs objects, :class:~mne.Evoked objects have evoked.times, :attr:evoked.ch_names <mne.Evoked.ch_names>, and :class:info <mne.Info> attributes.

Loading and saving Evoked data

Single :class:~mne.Evoked objects can be saved to disk with the :meth:evoked.save() <mne.Evoked.save> method. One difference between :class:~mne.Evoked objects and the other data structures is that multiple :class:~mne.Evoked objects can be saved into a single .fif file, using :func:mne.write_evokeds. The example data <sample-dataset> includes just such a .fif file: the data have already been epoched and averaged, and the file contains separate :class:~mne.Evoked objects for each experimental condition:

Notice that :func:mne.read_evokeds returned a :class:list of :class:~mne.Evoked objects, and each one has an evoked.comment attribute describing the experimental condition that was averaged to generate the estimate:

If you want to load only some of the conditions present in a .fif file, :func:~mne.read_evokeds has a condition parameter, which takes either a string (matched against the comment attribute of the evoked objects on disk), or an integer selecting the :class:~mne.Evoked object based on the order it's stored in the file. Passing lists of integers or strings is also possible. If only one object is selected, the :class:~mne.Evoked object will be returned directly (rather than a length-one list containing it):

Above, when we created an :class:~mne.Evoked object by averaging epochs, baseline correction was applied by default when we extracted epochs from the class:~mne.io.Raw object (the default baseline period is (None, 0), which assured zero mean for times before the stimulus event). In contrast, if we plot the first :class:~mne.Evoked object in the list that was loaded from disk, we'll see that the data have not been baseline-corrected:

This can be remedied by either passing a baseline parameter to :func:mne.read_evokeds, or by applying baseline correction after loading, as shown here:

Notice that :meth:~mne.Evoked.apply_baseline operated in-place. Similarly, :class:~mne.Evoked objects may have been saved to disk with or without :term:projectors <projector> applied; you can pass proj=True to the :func:~mne.read_evokeds function, or use the :meth:~mne.Evoked.apply_proj method after loading.

Combining Evoked objects

One way to pool data across multiple conditions when estimating evoked responses is to do so prior to averaging (recall that MNE-Python can select based on partial matching of /-separated epoch labels; see tut-section-subselect-epochs for more info):

This approach will weight each epoch equally and create a single :class:~mne.Evoked object. Notice that the printed representation includes (average, N=145), indicating that the :class:~mne.Evoked object was created by averaging across 145 epochs. In this case, the event types were fairly close in number:

However, this may not always be the case; if for statistical reasons it is important to average the same number of epochs from different conditions, you can use :meth:~mne.Epochs.equalize_event_counts prior to averaging.

Another approach to pooling across conditions is to create separate :class:~mne.Evoked objects for each condition, and combine them afterward. This can be accomplished by the function :func:mne.combine_evoked, which computes a weighted sum of the :class:~mne.Evoked objects given to it. The weights can be manually specified as a list or array of float values, or can be specified using the keyword 'equal' (weight each ~mne.Evoked object by $\frac{1}{N}$, where $N$ is the number of ~mne.Evoked objects given) or the keyword 'nave' (weight each ~mne.Evoked object proportional to the number of epochs averaged together to create it):

Note that the nave attribute of the resulting ~mne.Evoked object will reflect the effective number of averages, and depends on both the nave attributes of the contributing ~mne.Evoked objects and the weights at which they are combined. Keeping track of effective nave is important for inverse imaging, because nave is used to scale the noise covariance estimate (which in turn affects the magnitude of estimated source activity). See minimum_norm_estimates for more information (especially the whitening_and_scaling section). Note that mne.grand_average does not adjust nave to reflect effective number of averaged epochs; rather it simply sets nave to the number of evokeds that were averaged together. For this reason, it is best to use mne.combine_evoked rather than mne.grand_average if you intend to perform inverse imaging on the resulting :class:~mne.Evoked object.

Other uses of Evoked objects

Although the most common use of :class:~mne.Evoked objects is to store averages of epoched data, there are a couple other uses worth noting here. First, the method :meth:epochs.standard_error() <mne.Epochs.standard_error> will create an :class:~mne.Evoked object (just like :meth:epochs.average() <mne.Epochs.average> does), but the data in the :class:~mne.Evoked object will be the standard error across epochs instead of the average. To indicate this difference, :class:~mne.Evoked objects have a :attr:~mne.Evoked.kind attribute that takes values 'average' or 'standard error' as appropriate.

Another use of :class:~mne.Evoked objects is to represent a single trial or epoch of data, usually when looping through epochs. This can be easily accomplished with the :meth:epochs.iter_evoked() <mne.Epochs.iter_evoked> method, and can be useful for applications where you want to do something that is only possible for :class:~mne.Evoked objects. For example, here we use the :meth:~mne.Evoked.get_peak method (which isn't available for :class:~mne.Epochs objects) to get the peak response in each trial:

.. REFERENCES