[Mne_analysis] Unbalanced number of trials between individuals: problem in sources

Laetitia Grabot laetitia.grabot at gmail.com
Thu Mar 3 11:02:02 EST 2016
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Ok, thanks, I was still applying the old advice of equalizing epochs
between conditions! But so, why in certain statistical examples of the
website, you're still equalizing conditions (like in the Reapeated-measures
ANOVA in sources)?

2016-03-03 16:43 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:

> Yes that's the idea. Amplitudes of the inverse solution are scaled by the
> number of trials (.nave). This should work as an heuristic unless you
> compare rare to frequent events (e.g. oddball).
> Hope this helps.
>
> On Thu, Mar 3, 2016 at 4:38 PM, Laetitia Grabot <laetitia.grabot at gmail.com
> > wrote:
>
>> So does that mean that we need not to equalize epochs between conditions
>> (within one subject)?
>>
>> 2016-03-03 16:23 GMT+01:00 Denis-Alexander Engemann <
>> denis.engemann at gmail.com>:
>>
>>> Hi Laetitia,
>>>
>>> I I'm understanding you correctly this should not be an issue for evoked
>>> data as long as you are using the .average method that will tell the
>>> inverse routines how to scale the data via its .nave attribute.
>>> Did you have any particular problems?
>>>
>>> Cheers,
>>> Denis
>>>
>>> On Thu, Mar 3, 2016 at 3:09 PM, Laetitia Grabot <
>>> laetitia.grabot at gmail.com> wrote:
>>>
>>>> Dear MNE-users,
>>>>
>>>> I am aware of the fact that when contrasting two conditions, the epochs
>>>> within each condition should be equalized before reconstructing in source
>>>> space, otherwise the SNR would be different between the two conditions.
>>>> That also means that if two subjects have a different number of epochs, the
>>>> SNR of each subject will be different. It is often the case that the number
>>>> of epochs doubled between subjects (50 vs. 100) so I guess that it is an
>>>> issue when looking at group averaged stc for instance.
>>>> A possible solution would be to normalize the stc to correct for the
>>>> number of trials. What do you think of that? What would be the proper
>>>> normalization?
>>>>
>>>> Thanks,
>>>> Best,
>>>> Laetitia G.
>>>>
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