[Mne_analysis] Grand average of evoked files?

Thu Sep 11 10:36:02 EDT 2014

Maria,

I think your trouble is related to the fact that you add together lists,
not evokeds.
The read_evokeds function returns a list by default. This is because it
assumes to find multiple evokeds in one file.

HTH,
Denis

2014-09-11 16:31 GMT+02:00 Maria Hakonen <maria.hakonen at gmail.com>:

> Hi,
>
> Thank you for answers!
>
> I tried Mainak's solution but it gives
>
> grand_ave = evoked1+evoked2 +evoked3
>
> grand_ave:
> [<Evoked  |  comment : 'Unknown', time : [-0.099994, 0.499968], n_epochs :
> 160, n_channels x n_times : 306 x 721>,
>  <Evoked  |  comment : 'Unknown', time : [-0.099994, 0.499968], n_epochs :
> 160, n_channels x n_times : 306 x 721>,
>  <Evoked  |  comment : 'Unknown', time : [-0.099994, 0.499968], n_epochs :
> 160, n_channels x n_times : 306 x 721>]
>
> I think that I should get only one evoked if this works correctly.
>
> Maybe I should try Stephen's solution if there is no simpler ones.
>
> -Maria
>
>
> 2014-09-11 14:58 GMT+03:00 Stephen Politzer-Ahles <spa268 at nyu.edu>:
>
>> Hello Maria,
>>
>> I haven't done this with evoked sensor data, only with STCs. However, as
>> long as evoked data also have an 'add' method, I imagine something like
>> this should work for you as well:
>>
>>
>>
>> stc_avgs = dict()
>>
>>
>> # get a grand average for each condition (I defined a dictionary of
>> conditions earlier)
>> for condition in conditions.keys():
>>
>> # where all my STCs for each participant in this condition are stored
>> stcdir = os.environ['SAMPLE'] + '/stcs_ica/' + condition
>>
>> # template for the names of the STCs (I'm only doing left hemi here)
>> fname = os.path.join( stcdir, '%s-' + condition + '-free-lh.stc' )
>>
>> # use a list comprehension to read in the STC for each subject; I defined
>> a list of participants earlier)
>> stcs = [mne.read_source_estimate(fname % subject) for subject in
>> participants]
>>
>> # math out the grand average
>> stc_avg = reduce(add, stcs)
>> stc_avg /= len(stcs)
>>
>> # put the grand average for this condition into a dictionary of all the
>> conditions
>> stc_avg.subject = 'fsaverage'
>> stc_avgs[condition] = stc_avg
>>
>>
>>
>> (however, Mainak's solution, if it works, looks much simpler!)
>>
>>
>>
>>
>>
>> Stephen Politzer-Ahles
>> New York University, Abu Dhabi
>> Neuroscience of Language Lab
>> http://www.nyu.edu/projects/politzer-ahles/
>>
>> On Thu, Sep 11, 2014 at 3:47 PM, Maria Hakonen <maria.hakonen at gmail.com>
>> wrote:
>>
>>> Hi all,
>>>
>>> I would like to compute a grand average of evoked files.
>>>
>>> It seems to work as: grand_ave = np.mean([evoked1.data, evoked2.data,
>>> evoked3.data],1)
>>>
>>> However, the problem is that if the evoked data is saved as:
>>>
>>> evoked.save("filename-ave.fif")
>>>
>>> and loaded as:
>>>
>>> evoked = mne.read_evokeds("filename-ave.fif")
>>>
>>> evoked doesn't have attribute data.
>>>
>>> I also tried:
>>> grand_ave = np.mean([evoked1, evoked2, evoked3],1)
>>>
>>> but this gives an error:
>>>
>>> TypeError                                 Traceback (most recent call
>>> last)
>>> /scratch/braindata/mhhakone/intell/<ipython-input-65-f6e26a212f6a> in
>>> <module>()
>>> ----> 1 grand_ave = np.mean([evoked1, evoked2, evoked3],1)
>>>
>>> /usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.pyc in mean(a,
>>> axis, dtype, out)
>>>    2371         mean = a.mean
>>>    2372     except AttributeError:
>>> -> 2373         return _wrapit(a, 'mean', axis, dtype, out)
>>>    2374     return mean(axis, dtype, out)
>>>    2375
>>>
>>> /usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.pyc in
>>> _wrapit(obj, method, *args, **kwds)
>>>      35     except AttributeError:
>>>      36         wrap = None
>>> ---> 37     result = getattr(asarray(obj),method)(*args, **kwds)
>>>      38     if wrap:
>>>      39         if not isinstance(result, mu.ndarray):
>>>
>>> TypeError: unsupported operand type(s) for /: 'Evoked' and 'float'
>>>
>>>
>>>
>>> How can I get data from -ave.fif files? (The -ave.fif files I have saved
>>> seems to open correctly in xFit, Matlab and mne_analyze.)
>>> Or is there some better way to calculate the grand average of -ave.fif
>>> files?
>>>
>>> Thanks already in advance!
>>>
>>> Regards,
>>> Maria
>>>
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