[FieldTrip] group average over time frequency analysis

[Tzvetan Popov]

Dear Philip,

In that case you should consult the function ft_freqbaseline. It offers a variety of ways to normalise the data prior to grand averaging.
Good luck,
tzvetan


> On 6 Mar 2022, at 12:24, philip Joadavi <p.joadavi at gmail.com> wrote:
> 
> Dear Tzvetan,
> 
> Thanks for your reply. sorry I made a mistake in typing. I meant the function  FT_FREQGRANDAVERAGE  (time-frequency spectrum
>  over multiple subjects). I want to average the time-frequency spectrum over 11 subjects. For averaging, I want to be sure if the function normalizes the power spectrum for each subject before averaging (to prevent the influence of one subject on the mean value).  
> do you know if the function (ft_freqgrandaverage) does this, or is there another function that I need to use for normalizing the time-frequency spectrum for each subject separately?
> 
> Thanks a lot!
> Philip
> 
>  
> 
> On Fri, Mar 4, 2022 at 5:26 PM Tzvetan Popov <tzvetan.popov at uni-konstanz.de <mailto:tzvetan.popov at uni-konstanz.de>> wrote:
> Dear Philip,
> 
> The average over subjects is done by ft_timelockgrandaverage. Possible configuration option is  cfg.normalizevar = 'N' or 'N-1' (default = 'N-1’). You might consult the help of this function by typing help ft_timelockgrandaverage into the command window.
> If by ‘normalisation’ you mean some sort of baseline correction, you should apply ft_timelockbaseline for each participant prior to the average over participants. 
> Good luck,
> tzvetan
> 
> 
> 
>> On 4 Mar 2022, at 16:44, philip Joadavi via fieldtrip <fieldtrip at science.ru.nl <mailto:fieldtrip at science.ru.nl>> wrote:
>> 
>> Hi all,
>> 
>> I have a short question for ft_grandaverage. I want to get the average of time frequency results over my subjects, and I was wondering if the function ft_grandaverage does do the normalization over subjects when it gets the mean value, or do we need to do the subject normalization separately before calling this function? 
>> 
>> Thanks a lot!
>> Philip
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>> https://doi.org/10.1371/journal.pcbi.1002202 <https://doi.org/10.1371/journal.pcbi.1002202>
> 

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