Depends on exactly what you're trying to do, but that would be a good start. Hmmm... I'm suprised this option hasn't percolated into the Melodic GUI.
On Thursday, September 27, 2012 08:08:34 you wrote:
> Hi Ben,
>
> Is it
>
> melodic -i filtered_func_data -n 50 -o filtered_func_data.ca
>
> ?
>
> Thanks.
>
> Mark
>
> 2012/9/27 Benjamin Kay <[log in to unmask]>
>
> > Why are you generating 300 components? That's way too many for
> > interpretation! I'm guessing you let MELODIC try to pick the number of
> > components automatically. I have found that this often fails for group ICA
> > with less-than-pristine datasets. Try using the -n option to limit the
> > number of components to around 50 (or a # of your choice). As I recall,
> > this will discard all but the most 50 relevant PCA components prior to
> > running ICA.
> >
> > On Wednesday, September 26, 2012 21:59:14 you wrote:
> > > Hi,
> > >
> > > I believe this might be a simple question but I've checked previous
> > lists but still can not find answer......
> > >
> > > I am a new user of MELODIC and try to use its output data to remove
> > artefact in my subject's FMRI data. I did FSL's FEAT analysis and selected
> > MELODIC analysis in Pre-stats in one subject's long-run FMRI data (a
> > complex cognition task containing 800 volumes). Report of FEAT Pre-stats
> > showed that the translation of this subject is less than 1.5 mm. In MELODIC
> > report, it produced 300 independent components, which retains about 85% of
> > total variability. The first ~30 components captured about 40% of the
> > variance across the entire functional data.
> > >
> > > Is the number reasonable? The problem is, it will be grueling to go
> > through these 300 ICs to remove noise components! Could I just check the
> > first ~30 components to remove noise?
> > >
> > >
> > > Thanks for any help.
> > >
> > > Mark
> > >
> >
>
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