[Eeglablist] STUDY comparison over sessions

[Makoto Miyakoshi]

Dear Renan,

If C is within-subject condition that shared ICA and S is within-subject
but ICA not shared (they should not share it), this could be understood as
a mixed design, which is within-subject condition & group (i.e. treat it as
if it were a between-subject condition, but same distribution) condition.
You should set the STUDY.design in that way.

> 1) If I understood well, each cluster may include components from all S
sessions. How can I identify the session number each cluster component is
related to?

If you open each IC's ERP (for example) in the edit/plot cluster window,
you can see the info. Also, if you open STUDY.cluster(1,3).icaerp (or
something like that) for cluster 3, you can see something like {2,4} which
means you have 2 groups x 4 within-subject conditions (it depends on your
STUDY.design).

> 2) When plotting the dipoles, is it somehow possible to discriminate them
with respect to session number?

The supported method would be to make two designs, one with session 1 and
the other one with session 2. If you want to write code, it could be easier.

Makoto

On Tue, Oct 28, 2014 at 8:55 AM, Renan Moioli <renan.moioli at gmail.com>
wrote:

> Dear all,
>
> In my analysis, I have N subjects evaluated in C conditions in S sessions
> (some days apart from each other).
>
> After all the processing required for ICA components cluster analysis, I
> created a STUDY. My  goal is to investigate whether clusters are similar
> over sessions, both in dipole localisation and ERSP statistics. Thus, my
> STUDY design has all C conditions as ind. variable 1 and all S sessions as
> ind. variable 2.
>
> 1) If I understood well, each cluster may include components from all S
> sessions. How can I identify the session number each cluster component is
> related to?
>
> 2) When plotting the dipoles, is it somehow possible to discriminate them
> with respect to session number?
>
> Thank you very much for your help!
>
> Best regards,
>
> Renan
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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