The Epochs data structure: discontinuous data

This tutorial covers the basics of creating and working with epoched data. It introduces the Epochs data structure in detail, including how to load, query, subselect, export, and plot data from an Epochs object. For more information about visualizing Epochs objects, see Visualizing epoched data. For info on creating an Epochs object from (possibly simulated) data in a NumPy array, see Creating MNE-Python data structures from scratch.

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

import os
import mne

Epochs objects are a data structure for representing and analyzing equal-duration chunks of the EEG/MEG signal. Epochs are most often used to represent data that is time-locked to repeated experimental events (such as stimulus onsets or subject button presses), but can also be used for storing sequential or overlapping frames of a continuous signal (e.g., for analysis of resting-state activity; see Making equally-spaced Events arrays). Inside an Epochs object, the data are stored in an array of shape (n_epochs, n_channels, n_times).

Epochs objects have many similarities with Raw objects, including:

• They can be loaded from and saved to disk in .fif format, and their data can be exported to a NumPy array through the get_data() method or to a Pandas DataFrame through the to_data_frame() method.

• Both Epochs and Raw objects support channel selection by index or name, including pick(), pick_channels() and pick_types() methods.

• SSP projector manipulation is possible through add_proj(), del_proj(), and plot_projs_topomap() methods.

• Both Epochs and Raw objects have copy(), crop(), time_as_index(), filter(), and resample() methods.

• Both Epochs and Raw objects have times, ch_names, proj, and info attributes.

• Both Epochs and Raw objects have built-in plotting methods plot(), plot_psd(), and plot_psd_topomap().

Creating Epoched data from a Raw object

The example dataset we’ve been using thus far doesn’t include pre-epoched data, so in this section we’ll load the continuous data and create epochs based on the events recorded in the Raw object’s STIM channels. As we often do in these tutorials, we’ll crop() the Raw data to save memory:

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=60)

As we saw in the Parsing events from raw data tutorial, we can extract an events array from Raw objects using mne.find_events():

events = mne.find_events(raw, stim_channel='STI 014')

Out:

86 events found
Event IDs: [ 1  2  3  4  5 32]

Note

We could also have loaded the events from file, using mne.read_events():

sample_data_events_file = os.path.join(sample_data_folder,
                                       'MEG', 'sample',
                                       'sample_audvis_raw-eve.fif')
events_from_file = mne.read_events(sample_data_events_file)

See Reading and writing events from/to a file for more details.

The Raw object and the events array are the bare minimum needed to create an Epochs object, which we create with the mne.Epochs class constructor. However, you will almost surely want to change some of the other default parameters. Here we’ll change tmin and tmax (the time relative to each event at which to start and end each epoch). Note also that the Epochs constructor accepts parameters reject and flat for rejecting individual epochs based on signal amplitude. See the Rejecting Epochs based on channel amplitude section for examples.

epochs = mne.Epochs(raw, events, tmin=-0.3, tmax=0.7)

Out:

Not setting metadata
Not setting metadata
86 matching events found
Setting baseline interval to [-0.2996928197375818, 0.0] sec
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated

You’ll see from the output that:

• all 320 events were used to create epochs

• baseline correction was automatically applied (by default, baseline is defined as the time span from tmin to 0, but can be customized with the baseline parameter)

• no additional metadata was provided (see Working with Epoch metadata for details)

• the projection operators present in the Raw file were copied over to the Epochs object

If we print the Epochs object, we’ll also see a note that the epochs are not copied into memory by default, and a count of the number of epochs created for each integer Event ID.

print(epochs)

Out:

<Epochs |  86 events (good & bad), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~3.3 MB, data not loaded,
 '1': 20
 '2': 20
 '3': 20
 '32': 4
 '4': 18
 '5': 4>

Notice that the Event IDs are in quotes; since we didn’t provide an event dictionary, the mne.Epochs constructor created one automatically and used the string representation of the integer Event IDs as the dictionary keys. This is more clear when viewing the event_id attribute:

print(epochs.event_id)

Out:

{'1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '32': 32}

This time let’s pass preload=True and provide an event dictionary; our provided dictionary will get stored as the event_id attribute and will make referencing events and pooling across event types easier:

event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
              'visual/right': 4, 'face': 5, 'buttonpress': 32}
epochs = mne.Epochs(raw, events, tmin=-0.3, tmax=0.7, event_id=event_dict,
                    preload=True)
print(epochs.event_id)
del raw  # we're done with raw, free up some memory

Out:

Not setting metadata
Not setting metadata
86 matching events found
Setting baseline interval to [-0.2996928197375818, 0.0] sec
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 86 events and 601 original time points ...
1 bad epochs dropped
{'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, 'visual/right': 4, 'face': 5, 'buttonpress': 32}

Notice that the output now mentions “1 bad epoch dropped”. In the tutorial section Rejecting Epochs based on channel amplitude we saw how you can specify channel amplitude criteria for rejecting epochs, but here we haven’t specified any such criteria. In this case, it turns out that the last event was too close the end of the (cropped) raw file to accommodate our requested tmax of 0.7 seconds, so the final epoch was dropped because it was too short. Here are the drop_log entries for the last 4 epochs (empty lists indicate epochs that were not dropped):

print(epochs.drop_log[-4:])

Out:

((), (), (), ('TOO_SHORT',))

Note

If you forget to provide the event dictionary to the Epochs constructor, you can add it later by assigning to the event_id attribute:

epochs.event_id = event_dict

Basic visualization of Epochs objects

The Epochs object can be visualized (and browsed interactively) using its plot() method:

epochs.plot(n_epochs=10)

Notice that the individual epochs are sequentially numbered along the bottom axis and are separated by vertical dashed lines. Epoch plots are interactive (similar to raw.plot()) and have many of the same interactive controls as Raw plots. Horizontal and vertical scrollbars allow browsing through epochs or channels (respectively), and pressing ? when the plot is focused will show a help screen with all the available controls. See Visualizing epoched data for more details (as well as other ways of visualizing epoched data).

Subselecting epochs

Now that we have our Epochs object with our descriptive event labels added, we can subselect epochs easily using square brackets. For example, we can load all the “catch trials” where the stimulus was a face:

print(epochs['face'])

Out:

<Epochs |  4 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~10.2 MB, data loaded,
 'face': 4>

We can also pool across conditions easily, thanks to how MNE-Python handles the / character in epoch labels (using what is sometimes called “tag-based indexing”):

# pool across left + right
print(epochs['auditory'])
assert len(epochs['auditory']) == (len(epochs['auditory/left']) +
                                   len(epochs['auditory/right']))
# pool across auditory + visual
print(epochs['left'])
assert len(epochs['left']) == (len(epochs['auditory/left']) +
                               len(epochs['visual/left']))

Out:

<Epochs |  40 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~72.2 MB, data loaded,
 'auditory/left': 20
 'auditory/right': 20>
<Epochs |  39 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~70.5 MB, data loaded,
 'auditory/left': 20
 'visual/left': 19>

You can also pool conditions by passing multiple tags as a list. Note that MNE-Python will not complain if you ask for tags not present in the object, as long as it can find some match: the below example is parsed as (inclusive) 'right' or 'bottom', and you can see from the output that it selects only auditory/right and visual/right.

print(epochs[['right', 'bottom']])

Out:

<Epochs |  38 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~68.8 MB, data loaded,
 'auditory/right': 20
 'visual/right': 18>

However, if no match is found, an error is returned:

try:
    print(epochs[['top', 'bottom']])
except KeyError:
    print('Tag-based selection with no matches raises a KeyError!')

Out:

Tag-based selection with no matches raises a KeyError!

Selecting epochs by index

Epochs objects can also be indexed with integers, slices, or lists of integers. This method of selection ignores event labels, so if you want the first 10 epochs of a particular type, you can select the type first, then use integers or slices:

print(epochs[:10])    # epochs 0-9
print(epochs[1:8:2])  # epochs 1, 3, 5, 7

print(epochs['buttonpress'][:4])            # first 4 "buttonpress" epochs
print(epochs['buttonpress'][[0, 1, 2, 3]])  # same as previous line

Out:

<Epochs |  10 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~20.5 MB, data loaded,
 'auditory/left': 2
 'auditory/right': 3
 'visual/left': 3
 'visual/right': 2>
<Epochs |  4 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~10.2 MB, data loaded,
 'visual/left': 2
 'visual/right': 2>
<Epochs |  4 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~10.2 MB, data loaded,
 'buttonpress': 4>
<Epochs |  4 events (all good), -0.299693 - 0.699283 sec, baseline -0.299693 – 0 sec, ~10.2 MB, data loaded,
 'buttonpress': 4>

Selecting, dropping, and reordering channels

You can use the pick(), pick_channels(), pick_types(), and drop_channels() methods to modify which channels are included in an Epochs object. You can also use reorder_channels() for this purpose; any channel names not provided to reorder_channels() will be dropped. Note that these channel selection methods modify the object in-place (unlike the square-bracket indexing to select epochs seen above) so in interactive/exploratory sessions you may want to create a copy() first.

epochs_eeg = epochs.copy().pick_types(meg=False, eeg=True)
print(epochs_eeg.ch_names)

new_order = ['EEG 002', 'STI 014', 'EOG 061', 'MEG 2521']
epochs_subset = epochs.copy().reorder_channels(new_order)
print(epochs_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', 'STI 014', 'EOG 061', 'MEG 2521']

del epochs_eeg, epochs_subset

Changing channel name and type

You can change the name or type of a channel using rename_channels() or set_channel_types(). Both methods take dictionaries 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.

epochs.rename_channels({'EOG 061': 'BlinkChannel'})

epochs.set_channel_types({'EEG 060': 'ecg'})
print(list(zip(epochs.ch_names, epochs.get_channel_types()))[-4:])

Out:

[('EEG 058', 'eeg'), ('EEG 059', 'eeg'), ('EEG 060', 'ecg'), ('BlinkChannel', 'eog')]

# let's set them back to the correct values before moving on
epochs.rename_channels({'BlinkChannel': 'EOG 061'})
epochs.set_channel_types({'EEG 060': 'eeg'})

Number of events	85
Events	auditory/left: 20 auditory/right: 20 buttonpress: 4 face: 4 visual/left: 19 visual/right: 18
Time range	-0.300 – 0.699 sec
Baseline	-0.300 – 0.000 sec

Selection in the time domain

To change the temporal extent of the Epochs, you can use the crop() method:

shorter_epochs = epochs.copy().crop(tmin=-0.1, tmax=0.1, include_tmax=True)

for name, obj in dict(Original=epochs, Cropped=shorter_epochs).items():
    print('{} epochs has {} time samples'
          .format(name, obj.get_data().shape[-1]))

Out:

Original epochs has 601 time samples
Cropped epochs has 121 time samples

Cropping removed part of the baseline. When printing the cropped Epochs, MNE-Python will inform you about the time period that was originally used to perform baseline correction by displaying the string “baseline period cropped after baseline correction”:

print(shorter_epochs)

Out:

<Epochs |  85 events (all good), -0.0998976 - 0.0998976 sec, baseline -0.299693 – 0 sec (baseline period was cropped after baseline correction), ~32.8 MB, data loaded,
 'auditory/left': 20
 'auditory/right': 20
 'buttonpress': 4
 'face': 4
 'visual/left': 19
 'visual/right': 18>

However, if you wanted to expand the time domain of an Epochs object, you would need to go back to the Raw data and recreate the Epochs with different values for tmin and/or tmax.

It is also possible to change the “zero point” that defines the time values in an Epochs object, with the shift_time() method. shift_time() allows shifting times relative to the current values, or specifying a fixed time to set as the new time value of the first sample (deriving the new time values of subsequent samples based on the Epochs object’s sampling frequency).

# shift times so that first sample of each epoch is at time zero
later_epochs = epochs.copy().shift_time(tshift=0., relative=False)
print(later_epochs.times[:3])

# shift times by a relative amount
later_epochs.shift_time(tshift=-7, relative=True)
print(later_epochs.times[:3])

Out:

[0.         0.00166496 0.00332992]
[-7.         -6.99833504 -6.99667008]

del shorter_epochs, later_epochs

Note that although time shifting respects the sampling frequency (the spacing between samples), it does not enforce the assumption that there is a sample occurring at exactly time=0.

Extracting data in other forms

The get_data() method returns the epoched data as a NumPy array, of shape (n_epochs, n_channels, n_times); an optional picks parameter selects a subset of channels by index, name, or type:

eog_data = epochs.get_data(picks='EOG 061')
meg_data = epochs.get_data(picks=['mag', 'grad'])
channel_4_6_8 = epochs.get_data(picks=slice(4, 9, 2))

for name, arr in dict(EOG=eog_data, MEG=meg_data, Slice=channel_4_6_8).items():
    print('{} contains {} channels'.format(name, arr.shape[1]))

Out:

EOG contains 1 channels
MEG contains 305 channels
Slice contains 3 channels

Note that if your analysis requires repeatedly extracting single epochs from an Epochs object, epochs.get_data(item=2) will be much faster than epochs[2].get_data(), because it avoids the step of subsetting the Epochs object first.

You can also export Epochs data to Pandas DataFrames. Here, the DataFrame index will be constructed by converting the time of each sample into milliseconds and rounding it to the nearest integer, and combining it with the event types and epoch numbers to form a hierarchical MultiIndex. Each channel will appear in a separate column. Then you can use any of Pandas’ tools for grouping and aggregating data; for example, here we select any epochs numbered 10 or less from the auditory/left condition, and extract times between 100 and 107 ms on channels EEG 056 through EEG 058 (note that slice indexing within Pandas’ loc is inclusive of the endpoint):

df = epochs.to_data_frame(index=['condition', 'epoch', 'time'])
df.sort_index(inplace=True)
print(df.loc[('auditory/left', slice(0, 10), slice(100, 107)),
             'EEG 056':'EEG 058'])

del df

Out:

channel                     EEG 056  ...    EEG 058
condition     epoch time             ...
auditory/left 2     100   18.814844  ...  16.245426
                    102   19.172632  ...  16.416629
                    103   20.186365  ...  17.215579
                    105   16.787378  ...  14.190982
                    107   12.970971  ...  11.337589
              6     100    1.007473  ...   2.413561
                    102   -0.602573  ...   1.614611
                    103   -2.749302  ...   0.701525
                    105   -7.460178  ...  -3.350294
                    107   -9.726169  ...  -5.347669
              10    100    4.922387  ...   1.976881
                    102    1.284874  ...  -1.618394
                    103    0.807824  ...  -1.390123
                    105    4.326073  ...   1.862746
                    107    5.220543  ...   2.262221

[15 rows x 3 columns]

See the Exporting Epochs to Pandas DataFrames tutorial for many more examples of the to_data_frame() method.

Loading and saving Epochs objects to disk

Epochs objects can be loaded and saved in the .fif format just like Raw objects, using the mne.read_epochs() function and the save() method. Functions are also available for loading data that was epoched outside of MNE-Python, such as mne.read_epochs_eeglab() and mne.read_epochs_kit().

epochs.save('saved-audiovisual-epo.fif', overwrite=True)
epochs_from_file = mne.read_epochs('saved-audiovisual-epo.fif', preload=False)

Out:

Reading /home/circleci/project/tutorials/epochs/saved-audiovisual-epo.fif ...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
    Found the data of interest:
        t =    -299.69 ...     699.28 ms
        0 CTF compensation matrices available
Not setting metadata
Not setting metadata
85 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
3 projection items activated

The MNE-Python naming convention for epochs files is that the file basename (the part before the .fif or .fif.gz extension) should end with -epo or _epo, and a warning will be issued if the filename you provide does not adhere to that convention.

As a final note, be aware that the class of the epochs object is different when epochs are loaded from disk rather than generated from a Raw object:

print(type(epochs))
print(type(epochs_from_file))

Out:

<class 'mne.epochs.Epochs'>
<class 'mne.epochs.EpochsFIF'>

In almost all cases this will not require changing anything about your code. However, if you need to do type checking on epochs objects, you can test against the base class that these classes are derived from:

print(all([isinstance(epochs, mne.BaseEpochs),
           isinstance(epochs_from_file, mne.BaseEpochs)]))

Out:

True

Iterating over Epochs

Iterating over an Epochs object will yield arrays rather than single-trial Epochs objects:

for epoch in epochs[:3]:
    print(type(epoch))

Out:

<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>

If you want to iterate over Epochs objects, you can use an integer index as the iterator:

for index in range(3):
    print(type(epochs[index]))

Out:

<class 'mne.epochs.Epochs'>
<class 'mne.epochs.Epochs'>
<class 'mne.epochs.Epochs'>

Estimated memory usage: 561 MB

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