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%matplotlib inline
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# Author: Jussi Nurminen (jnu@iki.fi)
#
# License: BSD (3-clause)
import mne
import os
from mne.datasets import multimodal
fname_raw = os.path.join(multimodal.data_path(), 'multimodal_raw.fif')
print(__doc__)
Read raw file
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raw = mne.io.read_raw_fif(fname_raw)
Check DACQ defined averaging categories and other info
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print(raw.acqparser)
Extract epochs corresponding to a category
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cond = raw.acqparser.get_condition(raw, 'Auditory right')
epochs = mne.Epochs(raw, **cond)
epochs.average().plot_topo(background_color='w')
Get epochs from all conditions, average
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evokeds = []
for cat in raw.acqparser.categories:
cond = raw.acqparser.get_condition(raw, cat)
# copy (supported) rejection parameters from DACQ settings
epochs = mne.Epochs(raw, reject=raw.acqparser.reject,
flat=raw.acqparser.flat, **cond)
evoked = epochs.average()
evoked.comment = cat['comment']
evokeds.append(evoked)
# save all averages to an evoked fiff file
# fname_out = 'multimodal-ave.fif'
# mne.write_evokeds(fname_out, evokeds)
Make a new averaging category
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newcat = dict()
newcat['comment'] = 'Visual lower left, longer epochs'
newcat['event'] = 3 # reference event
newcat['start'] = -.2 # epoch start rel. to ref. event (in seconds)
newcat['end'] = .7 # epoch end
newcat['reqevent'] = 0 # additional required event; 0 if none
newcat['reqwithin'] = .5 # ...required within .5 sec (before or after)
newcat['reqwhen'] = 2 # ...required before (1) or after (2) ref. event
newcat['index'] = 9 # can be set freely
cond = raw.acqparser.get_condition(raw, newcat)
epochs = mne.Epochs(raw, reject=raw.acqparser.reject,
flat=raw.acqparser.flat, **cond)
epochs.average().plot()