[Eeglablist] Coherence analysis on ICA components

[Agatha Lenartowicz]

Dear Susann.  The coherence bit seems like you have some feedback on.  However note that how you combine ICs is not trivial.  Imagine you have 4 occipital components: left, right, dorsal and ventral topography. You want to combine them bc they all show similar alpha responses.  One approach is to average the time series. Another - that you propose - is to average the mixing weights and retrieve new activations.  In my example an average of the mixing weights would place the topography centrally -- where none of the original had weighted.  So my new activations would be weighted heavily by locations that weakly contributed to the uncombined ICs.  This is all to say - keep an eye on what your combining is doing to your data.  I've struggled with this - have no clear solution - but am favoring very simple averaging where a single best IC is not available. Agatha


  
Sent from my phone. 

On Mar 4, 2012, at 14:29, Susann Sgorzaly <susann.sgorzaly at st.ovgu.de> wrote:

> Dear All,
> 
> I am working on my master thesis on EEG data and would like to do an 
> coherence analysis on ICA components. So my question is, has anyone 
> experience how to analyse it best?
> 
> My approach was:
> (1) Run ICA and identify task-related components.
> (2) If there are more than one component: average weights of these 
> components and recalculate activation matrix
> (3) Run coherence analysis on this new component with all other ICA 
> components
> (4) Perform a cluster analysis on those ICA components which are most 
> coherent with the averaged component off step (2) to see         if they 
> have a similar topography.
> 
> Is there a better way to perform coherence analysis on ICA components?
> 
> Thanks
> 
> Susann
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