[Mne_analysis] Grand mean across subjects

[Mikołaj Magnuski]

Let’s say you have a list of evokeds - evokeds. To get SEM you can do the
following:

import numpy as npfrom scipy.stats import sem

sem_data = sem(np.stack([x.data for x in evokeds], axis=2), axis=2)

Now sem_data is an array of size (n_channels, n_samples). To get the lower
bound of the SEM you would have to subtract sem_data form the data of your
grand average and add it to get the upper bound.
​

2016-08-24 12:23 GMT+02:00 Emanuela Liaci <emanuela.liaci at gmail.com>:

> Thanks for the response. A question remains: how can I get the standard
> error of the mean?
>
> Greetings
> Emanuela
>
> On Wed, Aug 17, 2016 at 9:25 PM, Alexandre Gramfort <
> alexandre.gramfort at telecom-paristech.fr> wrote:
>
>> hi,
>>
>> http://martinos.org/mne/dev/generated/mne.grand_average.html
>>
>> HTH
>> Alex
>>
>> On Tue, Aug 16, 2016 at 11:41 AM, Emanuela Liaci
>> <emanuela.liaci at gmail.com> wrote:
>> > Hi,
>> >
>> > I would like to calculate the grand mean and the standard error across
>> > subjects for each of my conditions. Is there a specific function in mne
>> to
>> > do it starting from each subject's EvokedArray ?
>> >
>> > Many thanks
>> > Emanuela
>> >
>> >
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