[Eeglablist] Coherence analysis on ICA components

Michał Bola michalbola at gmail.com
Mon Mar 5 14:07:05 PST 2012


Hi Susann,

I'm also working on between ICs functional connectivity, but use Granger
(toolbox written by Anil Seth) instead of coherence. As my experience is
rather modest, it would be great any of you, EEGlab specialists, can
comment on that to address our concerns. However my approach is as follows:

1. Run ICA and cluster
2. Identify task related clusters
3. For every subject identify ICs belonging to particular task related
cluster and for every patient create data matrix with ICs of interest only
(use functions functions: getclustcomps and eeg_getdatact)
4. For every subject apply Granger toolbox functions to the obtained ICs
matrix (if for a given subject there's more then one IC in the cluster, I
average the signal)

However, it now seems to me, that in this manner you can also run coherence
analysis (pop_newcrossf). But I have not tried yet.

Michal

PS: Susann, as we're working on similar issues, we can always meet in
Magdeburg o exchange experience. See you.

On 4 March 2012 23:29, 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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