[Mne_analysis] Grand average over subjects when bad channels are excluded?

[Alexandre Gramfort]

hi Maria,

your best option is to do a first for loop to get the list of all
channels to drop,
then drop them with evoked.drop_channels and then average.

also pay attention to the number of epochs if it varies, as the + operator
uses it.

any volunteer to add a mne.evoked.grand_average function?

HTH
Alex


On Mon, Nov 17, 2014 at 8:59 AM, Maria Hakonen <maria.hakonen at gmail.com> wrote:
> Hi all,
>
> I have computed grand average over subjects as follows:
>
> for subject in subjects:
>                 evoked = mne.read_evokeds(filename, baseline=(None, 0),
> proj=True,verbose=False)
>                 if flag == 1:
>                         evoked_all = evoked[0]
>                         flag = 0
>                 else:
>                         evoked_all = evoked_all+evoked[0]
>         evoked_all = evoked_all / len(subjects)
>
> However, a problem arises when the evoked files don't contain the same
> channels (this is because I have excluded bad channels and they are not same
> in all files):
>
> AssertionError: <Evoked  |  comment : 'Unknown', time : [-0.099994,
> 2.999808], n_epochs : 147, n_channels x n_times : 305 x 3721> and <Evoked  |
> comment : 'Unknown', time : [-0.099994, 2.999808], n_epochs : 154,
> n_channels x n_times : 306 x 3721> do not contain the same channels
>
> I wonder if there is any way to get the grand average over subjects if bad
> channels are excluded?
>
> Many thanks already in advance!
>
> Regards,
> Maria
>
>
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