[FieldTrip] Grandaverage after regressing out headposition confounds

["Jörn M. Horschig"]

Hi Danja,

you can could either compute the average yourself:
/data.avg = squeeze(nanmean(data.trial, 1));
     data.dimord = 'chan_time';/
Note that the covariance might be invalid here, not sure what 
ft_regressconfound does here.

or call /data = ft_checkdata(data, 'datatype', 'raw')/ which converts 
the timelocked data back to raw data format, and you can call 
/ft_timelockanalysis /again with /cfg.keeptrials = 'no'/ (the latter 
would be my favourite)

Regarding the planar gradient, I'd say no. Keep in mind that the planar 
gradient computation results in all values > 0, thus e.g. noise might 
not cancel out as efficiently and a simple t-test against with the 
classical null hypothesis becomes invalid. I don't do ERP analysis 
though, so if someone can provide a good reason for doing the 
transformation nonetheless, I might be convinced otherwise ;)

Best,
Jörn

On 7/20/2012 7:48 AM, Porada Danja wrote:
> Hi,
>
> I would like to compute a grand average of the individual ERFs after I already used ft_regressconfounds (in order to deal with changes in head position during the experiment).
>
> Here is the problem:
> For regressing out head position confounds I have to keep the individual trials during the timelockanalysis. But for computing the grand average afterwards I need averages and not individual trials. Which is the best way to obtain the average?
>
>
> And I have another question: Do I have to calculate the planar gradient before computing the grand average?
>
>
> Best,
> Danja
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> fieldtrip at donders.ru.nl
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-- 
Jörn M. Horschig
PhD Student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen
Neuronal Oscillations Group

P.O. Box 9101
NL-6500 HB Nijmegen
The Netherlands

Contact:
E-Mail: jm.horschig at donders.ru.nl
Tel:    +31-(0)24-36-68493
Web: http://www.ru.nl/donders

Visiting address:
Trigon, room 2.30
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NL-6525 EN Nijmegen
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