[Eeglablist] ERP localisation with BESA and DIPFIT with ICA

[Makoto Miyakoshi]

Dear Imali,

ICA returns 'one map/IC per a component' which does not change across
recording time.
A static location corresponds to a brain region.
If you think of averaged ERP topo, for example, scalp topography changes
from timepoint to timepoint. Independent components are not like that.

> 2.       **Do independent components for cognitive activity in brain
represents ERP components(P1,N1, etc)?

Not necessarily. One IC can explain 3 ERP peaks (P1/N1/P2 as one burst).

> 3.       **Since I have minimal(correct to say no..) experience in ERP,
how do I know my dipole localisations with ICA are correct? For instance,
in a visual task I would expect to see one or more dipoles in visual area,
but when changing the conditions  such as colour or shape where else do I
get dipoles? Or simply, how do I have a hypothesis for the ICA component
related dipoles?

How do I know my dipole location is correct?
When you calculate dipole fit, you'll have residual variance. If this value
is small, that means your dipole location is good.
For symmetrical two dipoles, when the topography show bilateral pattern you
should place two dipoles (This may require some prior knowledge about
somatosensory mu, alpha, and EOG).

> 4.       **With very limited neuroscience knowledge how do I get around
with localisations to extract a task related neuronal activity?

If you don't have time to read Scott Makeig, Arnaud Delorme, or Julie Onton
etc, then
1. run ICA
2. run dipfit (autofit)
Remember, 1 dipole for 1 (or bilateral 2) IC(s). They are always paired.
ICA generates time-invariant scalp topo, and dipfit calculates the
associated dipole(s) that is also time-invariant (ICs don't change their
locations throughout your data just as your brain regions don't).

If you have further questions please ask further.

Makoto

2012/8/31 IMALI THANUJA HETTIARACHCHI <ith at deakin.edu.au>

>  Dear EEGLAB list,****
>
> ** **
>
> While reading through papers for my experiments, I just became curious
> (with some confusion) on the dipole fitting approach of the ERP data(for a
> specific task). ****
>
> ** **
>
> According to my understanding the ERP wave consists of several components
> such as P1,N1, P2 , N2 and P3 mainly (stimulus dependent). As I am
> intending to use ICA based source localization(using DIPFIT plugin) I
> wanted to find out on what degree the two dipole fitting approaches are
> differing in BESA and  DIPFIT with ICA.****
>
> ** **
>
> **1.       **Am I correct if I say that with BESA, dipoles can be fitted
> to individual components of the ERP waveform? ****
>
> **2.       **Do independent components for cognitive activity in brain
> represents ERP components(P1,N1, etc)? ****
>
> **3.       **Since I have minimal(correct to say no..) experience in ERP,
> how do I know my dipole localisations with ICA are correct? For instance,
> in a visual task I would expect to see one or more dipoles in visual area,
> but when changing the conditions such as colour or shape where else do I
> get dipoles? Or simply, how do I have a hypothesis for the ICA component
> related dipoles?****
>
> **4.       **With very limited neuroscience knowledge how do I get around
> with localisations to extract a task related neuronal activity? ****
>
> ** **
>
> Sorry about throwing a lot of questions at the list, but I have always
> found EEGLAB list as very friendly and a very expertized group. So, your
> advice will be highly appreciated to move forward in my work.****
>
> ** **
>
> Best regards****
>
> Imali****
>
> ** **
>
> ****
>
> *Imali Thanuja Hettiarachchi*
>
> PhD Candidate****
>
> Centre for Intelligent Systems research****
>
> Deakin University, Geelong 3217, Australia.****
>
> Email: ith at deakin.edu.au
> www.deakin.edu.au/cisr****
>
> ** **
>
> [image: Description: Description: Description:
> cid:1216BE20-1800-4A47-8B9F-E7B9D94831CD at deakin.edu.au]****
>
> ** **
>
>
>
> ****
>
> ** **
>
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-- 
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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