The Evoked data structure: evoked/averaged data

This tutorial covers the basics of creating and working with evoked data. It introduces the Evoked data structure in detail, including how to load, query, subselect, export, and plot data from an Evoked object. For info on creating an Evoked object from (possibly simulated) data in a NumPy array, see Creating MNE’s data structures from scratch.

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

import os
import mne

Creating Evoked objects from Epochs

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

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)
events = mne.find_events(raw, stim_channel='STI 014')
# we'll skip the "face" and "buttonpress" conditions, to save memory:
event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
              'visual/right': 4}
epochs = mne.Epochs(raw, events, tmin=-0.3, tmax=0.7, event_id=event_dict,
                    preload=True)
evoked = epochs['auditory/left'].average()

del raw  # reduce memory usage

Out:

320 events found
Event IDs: [ 1  2  3  4  5 32]
Not setting metadata
Not setting metadata
289 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 289 events and 601 original time points ...
0 bad epochs dropped

Basic visualization of Evoked objects

We can visualize the average evoked response for left-auditory stimuli using the plot() method, which yields a butterfly plot of each channel type:

EEG (59 channels), Gradiometers (203 channels), Magnetometers (102 channels)

Like the plot() methods for Raw and Epochs objects, evoked.plot() has many parameters for customizing the plot output, such as color-coding channel traces by scalp location, or plotting the global field power alongside the channel traces. See Visualizing Evoked data for more information about visualizing Evoked objects.

Subselecting Evoked data

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

print(evoked.data[:2, :3])  # first 2 channels, first 3 timepoints

Out:

[[ 5.72160572e-13  3.57859354e-13  3.98040833e-13]
 [-2.75128428e-13 -3.15309907e-13 -5.83186429e-13]]

To select based on time in seconds, the 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, sample number, and sample index section of the tutorial about Raw data for details).

Selecting, dropping, and reordering channels

By default, when creating Evoked data from an 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 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 average() for details.

If you’ve already created the Evoked object, you can use the pick(), pick_channels(), pick_types(), and drop_channels() methods to modify which channels are included in an Evoked object. You can also use reorder_channels() for this purpose; any channel names not provided to 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 copy() first.

evoked_eeg = evoked.copy().pick_types(meg=False, eeg=True)
print(evoked_eeg.ch_names)

new_order = ['EEG 002', 'MEG 2521', 'EEG 003']
evoked_subset = evoked.copy().reorder_channels(new_order)
print(evoked_subset.ch_names)

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>
['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008', 'EEG 009', 'EEG 010', 'EEG 011', 'EEG 012', 'EEG 013', 'EEG 014', 'EEG 015', 'EEG 016', 'EEG 017', 'EEG 018', 'EEG 019', 'EEG 020', 'EEG 021', 'EEG 022', 'EEG 023', 'EEG 024', 'EEG 025', 'EEG 026', 'EEG 027', 'EEG 028', 'EEG 029', 'EEG 030', 'EEG 031', 'EEG 032', 'EEG 033', 'EEG 034', 'EEG 035', 'EEG 036', 'EEG 037', 'EEG 038', 'EEG 039', 'EEG 040', 'EEG 041', 'EEG 042', 'EEG 043', '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', 'EEG 060']
['EEG 002', 'MEG 2521', 'EEG 003']

Similarities among the core data structures

Evoked objects have many similarities with Raw and Epochs objects, including:

Loading and saving Evoked data

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

sample_data_evk_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                                    'sample_audvis-ave.fif')
evokeds_list = mne.read_evokeds(sample_data_evk_file, verbose=False)
print(evokeds_list)
print(type(evokeds_list))

Out:

[<Evoked | 'Left Auditory' (average, N=55), [-0.1998, 0.49949] sec, 376 ch, ~4.8 MB>, <Evoked | 'Right Auditory' (average, N=61), [-0.1998, 0.49949] sec, 376 ch, ~4.8 MB>, <Evoked | 'Left visual' (average, N=67), [-0.1998, 0.49949] sec, 376 ch, ~4.8 MB>, <Evoked | 'Right visual' (average, N=58), [-0.1998, 0.49949] sec, 376 ch, ~4.8 MB>]
<class 'list'>

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

Out:

Left Auditory
Right Auditory
Left visual
Right visual

If you want to load only some of the conditions present in a .fif file, 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 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 Evoked object will be returned directly (rather than a length-one list containing it):

right_vis = mne.read_evokeds(sample_data_evk_file, condition='Right visual')
print(right_vis)
print(type(right_vis))

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 (Right visual)
        0 CTF compensation matrices available
        nave = 58 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
<Evoked | 'Right visual' (average, N=58), [-0.1998, 0.49949] sec, 376 ch, ~4.8 MB>
<class 'mne.evoked.Evoked'>

Above, when we created an Evoked object by averaging epochs, baseline correction was applied by default when we extracted epochs from the class: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 Evoked object in the list that was loaded from disk, we’ll see that the data have not been baseline-corrected:

evokeds_list[0].plot(picks='eeg')
EEG (59 channels)

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

evokeds_list[0].apply_baseline((None, 0))
evokeds_list[0].plot(picks='eeg')
EEG (59 channels)

Notice that apply_baseline() operated in-place. Similarly, Evoked objects may have been saved to disk with or without projectors applied; you can pass proj=True to the read_evokeds() function, or use the 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 Subselecting epochs for more info):

left_right_aud = epochs['auditory'].average()
print(left_right_aud)

Out:

<Evoked | '0.50 * auditory/left + 0.50 * auditory/right' (average, N=145), [-0.29969, 0.69928] sec, 366 ch, ~5.3 MB>

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

left_aud = epochs['auditory/left'].average()
right_aud = epochs['auditory/right'].average()
print([evok.nave for evok in (left_aud, right_aud)])

Out:

[72, 73]

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 equalize_event_counts() prior to averaging.

Another approach to pooling across conditions is to create separate Evoked objects for each condition, and combine them afterward. This can be accomplished by the function mne.combine_evoked(), which computes a weighted sum of the 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 Evoked object by \(\frac{1}{N}\), where \(N\) is the number of Evoked objects given) or the keyword 'nave' (weight each Evoked object proportional to the number of epochs averaged together to create it):

Note that the nave attribute of the resulting Evoked object will reflect the effective number of averages, and depends on both the nave attributes of the contributing 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 The minimum-norm current 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 Evoked object.

Other uses of Evoked objects

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

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

for ix, trial in enumerate(epochs[:3].iter_evoked()):
    channel, latency, value = trial.get_peak(ch_type='eeg',
                                             return_amplitude=True)
    latency = int(round(latency * 1e3))  # convert to milliseconds
    value = int(round(value * 1e6))      # convert to µV
    print('Trial {}: peak of {} µV at {} ms in channel {}'
          .format(ix, value, latency, channel))

Out:

Trial 0: peak of 159 µV at 35 ms in channel EEG 003
Trial 1: peak of -45 µV at 569 ms in channel EEG 005
Trial 2: peak of -46 µV at 648 ms in channel EEG 015

Total running time of the script: ( 0 minutes 41.575 seconds)

Estimated memory usage: 756 MB

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