[Eeglablist] Coherence analysis on ICA components

[Tim Mullen]

Susann et al,

You may wish to use EEGLAB's SIFT plugin to perform multivariate
(partial) coherence and/or Granger causal analysis on your
ICA-decomposed EEGLAB datasets via multivariate autoregressive model
fitting:

http://sccn.ucsd.edu/wiki/SIFT

There is support for a wide variety of multivariate (and pairwise)
coherence/GC measures (here is a partial listing:
http://sccn.ucsd.edu/wiki/Chapter_4.3._A_partial_list_of_VAR-based_spectral,_coherence_and_GC_estimators)
as well as model validation, significance testing, and visualization.
Soon we'll be adding a module for group-level connectivity inference
via hierarchical bayesian modeling. Performing bivariate coherence or
bivariate granger-causal analysis on multivariate neuronal time series
should be treated with caution due to the potentially high false
positive rate as a consequence of common and mediated causes within a
moderately connected neuronal network. As such, it's often preferable
to adopt a multivariate modeling approach whenever possible.

Since SIFT is integrated with EEGLAB as a plugin, you can operate
naturally on EEGLAB datasets without requiring any import/export to
third-party toolboxes.

Good luck,

----------------------------------------------------------------
Tim Mullen

Swartz Center for Computational Neuroscience
Institute for Neural Computation
and Department of Cognitive Science
UC San Diego, La Jolla, CA, USA, Earth
w: http://www.antillipsi.net/



On Wed, Mar 7, 2012 at 1:57 AM, Marco Congedo <marco.congedo at gmail.com> wrote:
> Hello,
>
>   neither averaging weights nor averaging source time series is a valid
> approach,
> and this is true in general, not just for coherence analysis.
> Note that ICA weights and time series have arbitrary scaling,
> thus averaging them makes no sense.
> Either you perform coherence analysis for pairs of components,
> or, if you are inetrested in coherence of clusters of components,
> you have to use multivariate coherence measures.
>
> See the following paper for multivariate coherence definitions:
> http://arxiv.org/abs/0711.1455
>
> A paper on BSS coherence analysis can be found here:
> http://hal.archives-ouvertes.fr/index.php?action_todo=search&view_this_doc=hal-00423717&version=1&halsid=jbd4j01nu66tdhdoaoc37feqt6
>
> A software for computing (group) BSS and pairwise (instantaneous and lagged)
> coherence as described in the latter paper is available here:
> https://sites.google.com/site/marcocongedo/software/nica
>
> Hope this helps,
>
> _______________________________________________________________
>
> Marco CONGEDO,
>
>
>
> Research Scientist,
>
> Centre National de la Recherche Scientifique (cnrs) and Grenoble University.
>
>
>
> Team ViBS (Vision and Brain Signal Processing)
>
> GIPSA-lab (Grenoble Images Parole Signal Automatique)
>
>
>
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>
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>
>
>
> http://sites.google.com/site/marcocongedo
>
> _______________________________________________________________
>
>
>
>
>
>
> On Wed, Mar 7, 2012 at 3:24 AM, Agatha Lenartowicz <alenarto at ucla.edu>
> wrote:
>>
>>
>> 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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>
>
>
> --
> Marco Congedo
> http://sites.google.com/site/marcocongedo
>
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