[Eeglablist] On ICA based artifact rejection

Wed Sep 5 20:28:53 PDT 2012

Thank you very much Makoto, really appreciate your guidance and help.

I have further some questions regarding ICA artifact rejection and localisation.

In EEGLAb we use ICA for both artifact rejection and source localisation? As I want to use EEGLAB for my dipole localisation, I am a bit confused with the steps that I should follow, I read the details on artifact rejection given on the wiki and a  thread on 'Pipeline of processing to optimize ICA for artrifact removal' on the discussion list, but still not clear on the steps. Below I will briefly give the steps which I understood that I should follow, can you please tell me whether my understanding is correct and comment if I have gone wrong somewhere?

1.       Re-reference continues data (The data is collected on a bipolar montage so re-reference to the common average)

2.       Reject unsuitable portions of data by visual inspection

3.       High pass filter the data(cut-off @ 0.5Hz to preserve ERP components), low pass filter the data(cut-off 30Hz)

4.       Extract epochs (without baseline removal???)

5.       Run ICA

Now I get confused, after the ICA decomposition I will be able to view the ICA components with Tools> Reject data using ICA>Reject components by map

With this window I can detect the components for eye artifacts, muscle artifacts etc. Then is it,

6.       A. Tools>Remove components to subtract ICA components  or should I do

B. Tools> Reject data Epochs> reject data(all methods),( but if I do this how can that be an artifact rejection by ICA)  or

C.Tools>Reject data epochs>export marks to ICA reject and then Tools>Reject data epochs>Reject marked epochs?

Could you please make it clear to me how I should reject epochs using ICA after the first decomposition.

7.       Then Tools>Remove Baseline and   Plot>Channel ERP's steps will give me the ERP for a particular stimulation?

8.       Now to do dipole localisation Run ICA on the pruned data set and run DIPFIT, here won't I get the same remaining ICA components from the first epoched data set?

Many thanks,

Imali

From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
Sent: Wednesday, 5 September 2012 7:02 AM
To: IMALI THANUJA HETTIARACHCHI
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] ERP localisation with BESA and DIPFIT with ICA

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<mailto: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<mailto:ith at deakin.edu.au>
www.deakin.edu.au/cisr<http://www.deakin.edu.au/cisr>

[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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