Hello everybody,
I have a stupid question: I am running an experiment where for the
stimuli are different across sessions (let's say session A has only
condition A and session B has only condition B - of course it is not as
simple as that). For the purposes of my analysis I want to compare these
conditions and up to now I was first concatenating the sessions and then
averaging the concatenated file so everything was working fine. The
thing is that when moving to the Time Frequency domain, the size of the
concatenated file is large (~12gb) so averaging is *extremely* slow.
Since averaging works much much faster when applied to each session
separately, I would prefer to first average each session and then apply
the grand mean averaging across sessions. The problem which now appears
is that. as I said before, the sessions have different conditions and
Grand mean averaging complains about that. I know that I could maybe
bypass the problem via more elaborated scripting, but I think that
simplicity is a virtue and I thought to ask you whether you had any
ideas that will enable me to average each session separately and then
apply the grand mean averaging - maybe I could somehow alter the
condlist of each session to be the union of all possible conditions
(while in this session actually only one subset of this conditions
appears) and then apply the GM averaging without a problem?
Thanks and best,
Panagiotis
PS: I am using the latest update of SPM
--
Panagiotis S. Tsiatsis
Max Planck Institute for Biogical Cybernetics
Cognitive NeuroImaging Group
Tuebingen, Germany
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