[Mne_analysis] Grand average of evoked files?

Hari Bharadwaj hari at nmr.mgh.harvard.edu
Thu Sep 11 11:49:48 EDT 2014
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quick question: why np.sum(.) and not np.mean(.)?

On Thu, September 11, 2014 11:47 am, Denis-Alexander Engemann wrote:
> Hi,
>
> 2014-09-11 17:14 GMT+02:00 Maria Hakonen <maria.hakonen at gmail.com>:
>
>> Hi,
>>
>> Is there any function that returns the evoked data array?
>>
>
> If you have evokeds saved in a fiff you can just read them.
> If you know the exact condition you want you can pass it to the condition
> parameter in mne.read_evokeds and the evoked will be returned without the
> list.
>
> You can also compute the grand average on a list of evokeds
>
> grand_evoked = np.sum(evokeds)
>
>
>> I have made ica on epochs, evoked them and saved the evoked data. Can I
>> use the evoked files in mne python analyses (I would like to try for
>> example permutation t-test on source data with spatio-temporal
>> clustering
>> and plot grand averages) or should I make ica on raw data?
>>
>
> I would compute ICA on raw data which you can then apply at any later
> stage
> (raw, epochs, evoked).
>
> -Denis
>
>
>>
>> -Maria
>>
>>
>> 2014-09-11 17:36 GMT+03:00 Denis-Alexander Engemann <
>> denis.engemann at gmail.com>:
>>
>>> 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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>>>>>
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>>>>
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-- 
Hari Bharadwaj
Post-doctoral Associate,
Center for Computational Neuroscience
  and Neural Technology (CompNet),
Boston University
677 Beacon St.,
Boston, MA 02215

Martinos Center for Biomedical Imaging,
Massachusetts General Hospital
149 Thirteenth Street,
Charlestown, MA 02129

hari at nmr.mgh.harvard.edu
Ph: 734-883-5954
www.haribharadwaj.com

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