Note
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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, subset, export, and plot data from an
Evoked
object. For details on creating an Evoked
object from (possibly simulated) data in a NumPy array
, see Creating MNE-Python data structures from scratch.
As usual, we start by importing the modules we need:
import mne
Creating Evoked
objects from Epochs
#
Evoked
objects typically store EEG or MEG signals that have
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:
root = mne.datasets.sample.data_path() / 'MEG' / 'sample'
raw_file = root / 'sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(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
320 events found
Event IDs: [ 1 2 3 4 5 32]
Not setting metadata
289 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 289 events and 601 original time points ...
0 bad epochs dropped
You may have noticed that MNE informed us that “baseline correction” has been
applied. This happened automatically during creation of the
Epochs
object, but may also be initiated (or disabled)
manually. We will discuss this in more detail later.
The information about the baseline period of Epochs
is
transferred to derived Evoked
objects to maintain provenance as
you process your data:
print(f'Epochs baseline: {epochs.baseline}')
print(f'Evoked baseline: {evoked.baseline}')
Epochs baseline: (-0.2996928197375818, 0.0)
Evoked baseline: (-0.2996928197375818, 0.0)
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:
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 on visualizing
Evoked
objects.
Subsetting Evoked
data#
Unlike Raw
and Epochs
objects,
Evoked
objects do not support selection by square-bracket
indexing. Instead, data can be subsetted by indexing the
data
attribute:
print(evoked.data[:2, :3]) # first 2 channels, first 3 timepoints
[[ 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 on
Raw data for details).
Selecting, dropping, and reordering channels#
By default, when creating Evoked
data from an
Epochs
object, only the primary 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)
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:
They can be loaded from and saved to disk in
.fif
format, and their data can be exported to aNumPy array
(but through thedata
attribute instead of aget_data()
method).Pandas DataFrame
export is also available through theto_data_frame()
method.You can change the name or type of a channel using
evoked.rename_channels()
orevoked.set_channel_types()
. Both methods takedictionaries
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.SSP projector manipulation is possible through
add_proj()
,del_proj()
, andplot_projs_topomap()
methods, and theproj
attribute. See Repairing artifacts with SSP for more information on SSP.Like
Raw
andEpochs
objects,Evoked
objects havecopy()
,crop()
,time_as_index()
,filter()
, andresample()
methods.Like
Raw
andEpochs
objects,Evoked
objects haveevoked.times
,evoked.ch_names
, andinfo
attributes.
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 such a .fif
file: the data have already been epoched and
averaged, and the file contains separate Evoked
objects for
each experimental condition:
evk_file = root / 'sample_audvis-ave.fif'
evokeds_list = mne.read_evokeds(evk_file, verbose=False)
print(evokeds_list)
print(type(evokeds_list))
[<Evoked | 'Left Auditory' (average, N=55), -0.1998 – 0.49949 sec, baseline off, 376 ch, ~4.5 MB>, <Evoked | 'Right Auditory' (average, N=61), -0.1998 – 0.49949 sec, baseline off, 376 ch, ~4.5 MB>, <Evoked | 'Left visual' (average, N=67), -0.1998 – 0.49949 sec, baseline off, 376 ch, ~4.5 MB>, <Evoked | 'Right visual' (average, N=58), -0.1998 – 0.49949 sec, baseline off, 376 ch, ~4.5 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:
for evok in evokeds_list:
print(evok.comment)
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 is 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 inside a list of length one):
right_vis = mne.read_evokeds(evk_file, condition='Right visual')
print(right_vis)
print(type(right_vis))
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, baseline off, 376 ch, ~4.5 MB>
<class 'mne.evoked.Evoked'>
Previously, when we created an Evoked
object by averaging
epochs, baseline correction was applied by default when we extracted epochs
from the Raw
object (the default baseline period is (None, 0)
,
which ensures 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')
This can be remedied by either passing a baseline
parameter to
mne.read_evokeds()
, or by applying baseline correction after loading,
as shown here:
# Original baseline (none set)
print(f'Baseline after loading: {evokeds_list[0].baseline}')
# Apply a custom baseline correction
evokeds_list[0].apply_baseline((None, 0))
print(f'Baseline after calling apply_baseline(): {evokeds_list[0].baseline}')
# Visualize the evoked response
evokeds_list[0].plot(picks='eeg')
Baseline after loading: None
Applying baseline correction (mode: mean)
Baseline after calling apply_baseline(): (-0.19979521315838786, 0.0)
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 epoch labels separated by /
; see
Subselecting epochs for more information):
left_right_aud = epochs['auditory'].average()
left_right_aud
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:
[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 afterwards.
This can be accomplished with 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):
left_right_aud = mne.combine_evoked([left_aud, right_aud], weights='nave')
assert left_right_aud.nave == left_aud.nave + right_aud.nave
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 with 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 the effective
number of averaged epochs; 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 few 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 is not 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))
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 14.477 seconds)
Estimated memory usage: 507 MB