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%matplotlib inline
For a generic introduction to the computation of ERP and ERF
see tut_epoching_and_averaging. Here we cover the specifics
of EEG, namely:
- setting the reference
- using standard montages :func:`mne.channels.Montage`
- Evoked arithmetic (e.g. differences)
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import mne
from mne.datasets import sample
Setup for reading the raw data
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data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
raw = mne.io.read_raw_fif(raw_fname, add_eeg_ref=False, preload=True)
raw.set_eeg_reference() # set EEG average reference
Let's restrict the data to the EEG channels
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raw.pick_types(meg=False, eeg=True, eog=True)
By looking at the measurement info you will see that we have now 59 EEG channels and 1 EOG channel
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print(raw.info)
In practice it's quite common to have some EEG channels that are actually
EOG channels. To change a channel type you can use the
:func:mne.io.Raw.set_channel_types method. For example
to treat an EOG channel as EEG you can change its type using
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raw.set_channel_types(mapping={'EOG 061': 'eeg'})
print(raw.info)
And to change the nameo of the EOG channel
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raw.rename_channels(mapping={'EOG 061': 'EOG'})
Let's reset the EOG channel back to EOG type.
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raw.set_channel_types(mapping={'EOG': 'eog'})
The EEG channels in the sample dataset already have locations. These locations are available in the 'loc' of each channel description. For the first channel we get
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print(raw.info['chs'][0]['loc'])
And it's actually possible to plot the channel locations using
the :func:mne.io.Raw.plot_sensors method
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raw.plot_sensors()
raw.plot_sensors('3d') # in 3D
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montage = mne.channels.read_montage('standard_1020')
print(montage)
To apply a montage on your data use the :func:mne.io.set_montage
function. Here don't actually call this function as our demo dataset
already contains good EEG channel locations.
Next we'll explore the definition of the reference.
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raw_no_ref, _ = mne.io.set_eeg_reference(raw, [])
We next define Epochs and compute an ERP for the left auditory condition.
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reject = dict(eeg=180e-6, eog=150e-6)
event_id, tmin, tmax = {'left/auditory': 1}, -0.2, 0.5
events = mne.read_events(event_fname)
epochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,
reject=reject, add_eeg_ref=False)
evoked_no_ref = mne.Epochs(raw_no_ref, **epochs_params).average()
del raw_no_ref # save memory
title = 'EEG Original reference'
evoked_no_ref.plot(titles=dict(eeg=title))
evoked_no_ref.plot_topomap(times=[0.1], size=3., title=title)
Average reference: This is normally added by default, but can also be added explicitly.
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raw_car, _ = mne.io.set_eeg_reference(raw)
evoked_car = mne.Epochs(raw_car, **epochs_params).average()
del raw_car # save memory
title = 'EEG Average reference'
evoked_car.plot(titles=dict(eeg=title))
evoked_car.plot_topomap(times=[0.1], size=3., title=title)
Custom reference: Use the mean of channels EEG 001 and EEG 002 as a reference
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raw_custom, _ = mne.io.set_eeg_reference(raw, ['EEG 001', 'EEG 002'])
evoked_custom = mne.Epochs(raw_custom, **epochs_params).average()
del raw_custom # save memory
title = 'EEG Custom reference'
evoked_custom.plot(titles=dict(eeg=title))
evoked_custom.plot_topomap(times=[0.1], size=3., title=title)
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event_id = {'left/auditory': 1, 'right/auditory': 2,
'left/visual': 3, 'right/visual': 4}
epochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,
reject=reject)
epochs = mne.Epochs(raw, **epochs_params)
print(epochs)
Next, we create averages of stimulation-left vs stimulation-right trials. We can use basic arithmetic to, for example, construct and plot difference ERPs.
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left, right = epochs["left"].average(), epochs["right"].average()
# create and plot difference ERP
mne.combine_evoked([left, -right], weights='equal').plot_joint()
This is an equal-weighting difference. If you have imbalanced trial numbers,
you could also consider either equalizing the number of events per
condition (using
:meth:epochs.equalize_epochs_counts <mne.Epochs.equalize_event_counts).
As an example, first, we create individual ERPs for each condition.
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aud_l = epochs["auditory", "left"].average()
aud_r = epochs["auditory", "right"].average()
vis_l = epochs["visual", "left"].average()
vis_r = epochs["visual", "right"].average()
all_evokeds = [aud_l, aud_r, vis_l, vis_r]
print(all_evokeds)
This can be simplified with a Python list comprehension:
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all_evokeds = [epochs[cond].average() for cond in sorted(event_id.keys())]
print(all_evokeds)
# Then, we construct and plot an unweighted average of left vs. right trials
# this way, too:
mne.combine_evoked(all_evokeds,
weights=(0.25, -0.25, 0.25, -0.25)).plot_joint()
Often, it makes sense to store Evoked objects in a dictionary or a list - either different conditions, or different subjects.
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# If they are stored in a list, they can be easily averaged, for example,
# for a grand average across subjects (or conditions).
grand_average = mne.grand_average(all_evokeds)
mne.write_evokeds('/tmp/tmp-ave.fif', all_evokeds)
# If Evokeds objects are stored in a dictionary, they can be retrieved by name.
all_evokeds = dict((cond, epochs[cond].average()) for cond in event_id)
print(all_evokeds['left/auditory'])
# Besides for explicit access, this can be used for example to set titles.
for cond in all_evokeds:
all_evokeds[cond].plot_joint(title=cond)