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%matplotlib inline

The events and :class:`~mne.Annotations` data structures

Events and :class:~mne.Annotations are quite similar. This tutorial highlights their differences and similarities, and tries to shed some light on which one is preferred to use in different situations when using MNE.

Here are the definitions from the glossary.

events
    Events correspond to specific time points in raw data; e.g., triggers,
    experimental condition events, etc. MNE represents events with integers
    that are stored in numpy arrays of shape (n_events, 3). Such arrays are
    classically obtained from a trigger channel, also referred to as stim
    channel.

annotations
    An annotation is defined by an onset, a duration, and a string
    description. It can contain information about the experiment, but
    also details on signals marked by a human: bad data segments,
    sleep scores, sleep events (spindles, K-complex) etc.

Both events and :class:~mne.Annotations can be seen as triplets where the first element answers to when something happens and the last element refers to what it is. The main difference is that events represent the onset in samples taking into account the first sample value (:attr:raw.first_samp <mne.io.Raw.first_samp>), and the description is an integer value. In contrast, :class:~mne.Annotations represents the onset in seconds (relative to the reference orig_time), and the description is an arbitrary string. There is no correspondence between the second element of events and :class:~mne.Annotations. For events, the second element corresponds to the previous value on the stimulus channel from which events are extracted. In practice, the second element is therefore in most cases zero. The second element of :class:~mne.Annotations is a float indicating its duration in seconds.

See sphx_glr_auto_examples_io_plot_read_events.py for a complete example of how to read, select, and visualize events; and sphx_glr_auto_tutorials_plot_artifacts_correction_rejection.py to learn how :class:~mne.Annotations are used to mark bad segments of data.

An example of events and annotations

The following example shows the recorded events in sample_audvis_raw.fif and marks bad segments due to eye blinks.



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import os.path as op
import numpy as np

import mne

# Load the data
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)

First we'll create and plot events associated with the experimental paradigm:



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# extract the events array from the stim channel
events = mne.find_events(raw)

# Specify event_id dictionary based on the meaning of experimental triggers
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
            'Visual/Left': 3, 'Visual/Right': 4,
            'smiley': 5, 'button': 32}
color = {1: 'green', 2: 'yellow', 3: 'red', 4: 'c', 5: 'black', 32: 'blue'}

mne.viz.plot_events(events, raw.info['sfreq'], raw.first_samp, color=color,
                    event_id=event_id)

Next, we're going to detect eye blinks and turn them into :class:~mne.Annotations:



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# find blinks
annotated_blink_raw = raw.copy()
eog_events = mne.preprocessing.find_eog_events(raw)
n_blinks = len(eog_events)

# Turn blink events into Annotations of 0.5 seconds duration,
# each centered on the blink event:
onset = eog_events[:, 0] / raw.info['sfreq'] - 0.25
duration = np.repeat(0.5, n_blinks)
description = ['bad blink'] * n_blinks
annot = mne.Annotations(onset, duration, description,
                        orig_time=raw.info['meas_date'])
annotated_blink_raw.set_annotations(annot)

# plot the annotated raw
annotated_blink_raw.plot()

Working with Annotations

An important element of :class:~mne.Annotations is orig_time which is the time reference for the onset. It is key to understand that when calling :func:raw.set_annotations <mne.io.Raw.set_annotations>, given annotations are copied and transformed so that :class:raw.annotations.orig_time <mne.Annotations> matches the recording time of the raw object. Refer to the documentation of :class:~mne.Annotations to see the expected behavior depending on meas_date and orig_time. Where meas_date is the recording time stored in :class:Info <mne.Info>. You can find more information about :class:Info <mne.Info> in sphx_glr_auto_tutorials_plot_info.py.

We'll now manipulate some simulated annotations. The first annotations has orig_time set to None while the second is set to a chosen POSIX timestamp for illustration purposes. Note that both annotations have different onset values.



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# Create an annotation object with orig_time undefined (default)
annot_none = mne.Annotations(onset=[0, 2, 9], duration=[0.5, 4, 0],
                             description=['foo', 'bar', 'foo'],
                             orig_time=None)
print(annot_none)

# Create an annotation object with orig_time
orig_time = '2002-12-03 19:01:31.676071'
annot_orig = mne.Annotations(onset=[22, 24, 31], duration=[0.5, 4, 0],
                             description=['foo', 'bar', 'foo'],
                             orig_time=orig_time)
print(annot_orig)

Now we create two raw objects and set each with different annotations. Then we plot both raw objects to compare the annotations.



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# Create two cropped copies of raw with the two previous annotations
raw_a = raw.copy().crop(tmax=12).set_annotations(annot_none)
raw_b = raw.copy().crop(tmax=12).set_annotations(annot_orig)

# Plot the raw objects
raw_a.plot()
raw_b.plot()

Note that although the onset values of both annotations were different, due to complementary orig_time they are now identical. This is because the first one (annot_none), once set in raw, adopted its orig_time. The second one (annot_orig) already had an orig_time, so its orig_time was changed to match the onset time of the raw. Changing an already defined orig_time of annotations caused its onset to be recalibrated with respect to the new orig_time. As a result both annotations have now identical onset and identical orig_time:



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# Show the annotations in the raw objects
print(raw_a.annotations)
print(raw_b.annotations)

# Show that the onsets are the same
np.set_printoptions(precision=6)
print(raw_a.annotations.onset)
print(raw_b.annotations.onset)

Notice again that for the case where orig_time is None, it is assumed that the orig_time is the time of the first sample of data.



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raw_delta = (1 / raw.info['sfreq'])
print('raw.first_sample is {}'.format(raw.first_samp * raw_delta))
print('annot_none.onset[0] is {}'.format(annot_none.onset[0]))
print('raw_a.annotations.onset[0] is {}'.format(raw_a.annotations.onset[0]))

It is possible to concatenate two annotations with the + operator (just like lists) if both share the same orig_time



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annot = mne.Annotations(onset=[10], duration=[0.5],
                        description=['foobar'],
                        orig_time=orig_time)
annot = annot_orig + annot  # concatenation
print(annot)

Note that you can also save annotations to disk in FIF format::

>>> annot.save('my-annot.fif')

Or as CSV with onsets in (absolute) ISO timestamps::

>>> annot.save('my-annot.csv')

Or in plain text with onsets relative to orig_time::

>>> annot.save('my-annot.txt')

The events and :class:~mne.Annotations data structures

An example of events and annotations

Working with Annotations

The events and :class:`~mne.Annotations` data structures