[Eeglablist] Merging ICA components

[Makoto Miyakoshi]

Dear Robert,

> Our previous research has shown mu (lack of mu suppression) to have
frontal, temporal, central and limbic aspects that are disrupted
bilaterally due to multiple connectivity deficits across these regions.

This is interesting. If the mu network is so big, and the EEG sources moves
around along with it rather than the entire network 'flashes' in synch, the
ICA would return subspaces rather than a single decomposition. We often
observe it with occipital alpha.

Makoto

On Thu, Mar 17, 2016 at 5:24 AM, robert coben <drcoben at gmail.com> wrote:

> Dave,
>
> There is a good reason you are getting multiple components as the mu
> rhythm is multiply determined and has various aspects. Our previous
> research has shown mu (lack of mu suppression) to have frontal, temporal,
> central and limbic aspects that are disrupted bilaterally due to multiple
> connectivity deficits across these regions. What you see centrally at the
> surface is the result of these multiple phenomena. So, having multiple
> components is not a problem to collapse it is the reality of what you are
> studying.
>
> Rob
>
> On Jan 19, 2016, at 12:33 PM, Jenson, Dave <djenson1 at uthsc.edu> wrote:
>
> All,
>
> Does anyone know how to merge components from an ICA decomposition into a
> single component?  I am running a study on the sensorimotor mu rhythm, and
> I have a few subjects for whom ICA produces multiple potential components.
> At the moment I am choosing the best component per subject based on scalp
> map, spectra, and dipole location.  However, I still have a few subjects
> for whom there is no clear winner.  I don't want to lose statistical power
> by excluding a component that represents the sensorimotor mu, but also
> don't want to disproportionately weight some subjects over others at the
> study level by including multiple components.  Is there a way to 1) merge
> components or 2) overcome this problem in a different manner?
>
> -Dave Jenson
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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