[Eeglablist] On ICA based artifact rejection
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Thu Sep 6 15:58:11 PDT 2012
Dear Imali,
Pipeline check?
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)
I would say filtering should be done before data rejection, since the
rejection creates boundaries which can confuse filtering.
4. **Extract epochs (without baseline removal???)
According to Groppe et al., whole-epoch baseline is better than usual short
pre-stimulus baseline. Scott says no baseline correction is even better. So
without baseline removal may be better.
5. **Run ICA
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*?
6A is not necessary if you are going to use STUDY (it will create clusters
for artifacts) so don't bother to do this. 6B is recommended.
Theoretically, 2nd ICA after 6B improves the quality of decomposition, but
in my experience it rarely changes the result unless drastic rejection,
either quantitatively or qualitatively, is performed.
Could you please make it clear to me how I should reject epochs using ICA
after the first decomposition.****
Just take a look at independent component activities as you check your
channel EEG data. It is a good idea to use 'all methods' for obtaining
statistical suggestions (but don't take them blindly). You may want to
discard 5-10 % of data here, depending your data quality. Improbability
test is good but hopefully it is done after thresholding on channel EEG
(therefore I recommend mild amplitude threshold on channel EEG before ICA,
and then improbability test on IC activities).
*7. ***Then *Tools>Remove Baseline* and * Plot>Channel ERP’s *steps*
*will give me the ERP for a particular stimulation?
Yes.
***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?
Again, whether or not running the 2nd ICA depends on how much you care
about data quality. DIPFIT does not care your IC activities. It only cares
about scalp maps ICA generated. Therefore, epoch rejection does not affect
DIPFIT performance.
You should locate two dipoles manually when you find bilateral topographies
using an interactive 'fine fit' GUI of DIPFIT.
Makoto
2012/9/6 Stephen Politzer-Ahles <politzerahless at gmail.com>
> Hi Imali,
>
> I don't have experience with using "reject based on ICA", but the first
> option you pointed out (6A--using Tools>Reject components to remove the
> IC(s) with artifact) works. What I have typically done is first use
> Tools>Reject components to do that, and then use Tools>Reject epochs (by
> inspection) on the cleaned data to go through and reject any epochs that
> contain other artifact. (In my case, I use ICA to remove the blink
> artifacts, but then must reject by inspection to remove artifact that's
> left over such as skin potentials or EMG).
>
> Maybe some others on the list can give you some more information about the
> other methods, which I am not familiar with.
>
> Best,
> Steve
>
>
> On Wed, Sep 5, 2012 at 10:28 PM, IMALI THANUJA HETTIARACHCHI <
> ith at deakin.edu.au> wrote:
>
>> 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>****
>>
>> 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]****
>>
>> ****
>>
>> ** **
>>
>> ****
>>
>>
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to
>> eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu****
>>
>>
>>
>> ****
>>
>> ** **
>>
>> --
>> Makoto Miyakoshi
>> JSPS Postdoctral Fellow for Research Abroad
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego****
>>
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to
>> eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>>
>
>
>
> --
> Stephen Politzer-Ahles
> University of Kansas
> Linguistics Department
> http://www.linguistics.ku.edu/
>
--
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20120906/b1a85471/attachment.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: image001.jpg
Type: image/jpeg
Size: 29155 bytes
Desc: not available
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20120906/b1a85471/attachment.jpg>
More information about the eeglablist
mailing list