[FieldTrip] Grandaverage after regressing out headposition confounds
"Jörn M. Horschig"
jm.horschig at donders.ru.nl
Fri Jul 20 11:59:37 CEST 2012
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 ;)
On 7/20/2012 7:48 AM, Porada Danja wrote:
> 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?
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
Jörn M. Horschig
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
E-Mail: jm.horschig at donders.ru.nl
Trigon, room 2.30
NL-6525 EN Nijmegen
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