[Mne_analysis] Averaging over channels

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Thu Dec 8 03:42:46 EST 2016
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there is indeed no out of the box solution to make a new dataset with this, yet
the plot_compare_evokeds allow you to view groups of channels


See the picks parameter.


On Wed, Dec 7, 2016 at 5:09 PM, Stephen Johnston
<sjayejohnston at gmail.com> wrote:
> Hi All
> I'm new at using MNE. Like it so far, but struggling a little getting
> access/manipulating EEG channel data.
> So far I have managed to get through pre-processing and have managed to
> extract my channels of interest, in this case 'Oz', 'O1', 'O2' and 'Pz' and
> calculated my evoked potentials for my events of interest, an example of how
> I'm doing that, for one event type follows...
> picks = [raw.ch_names.index(ch) for ch in ['OZ', 'O1', 'O2','PZ']]
> reject = dict(eeg=50e-6)
> event_id, tmin, tmax = {'deviant5': 5}, -0.2, 0.5
> epochs_params = dict(events=events, picks=picks, event_id=event_id,
> tmin=tmin, tmax=tmax,
>                      reject=None)
> evoked_deviant = mne.Epochs(raw, **epochs_params).average()
> The issue is (I'm calculating an MMN difference wave) - I want to average
> over the Occipital channels, to essentially make one 'new' channel, and I
> can't find the command/data to be able to do that.
> I'm sure it's simple, but I've been bashing my head a bit trying to figure
> it out.
> Any help much appreciated.
> Best
> Steve
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