[Eeglablist] eeglablist Digest, Vol 95, Issue 21
nirmala m
nimmims at gmail.com
Sun Sep 16 21:22:27 PDT 2012
Hi,
I recently started using EEGLAB so very basic questions what i am going
to ask....
Can i know the steps for interpolation?
I added (opened) the cnt file in the EEGLAB then i added the channel
location file also.
In EEGLAB tools>interpolate electrodes>select data from otherset>Data
index??
What is data index??
how to go about??
On Thu, Sep 13, 2012 at 10:38 PM, <eeglablist-request at sccn.ucsd.edu> wrote:
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> 1. Re: On ICA based artifact rejection (Tarik S Bel-Bahar)
>
>
> ---------- Forwarded message ----------
> From: Tarik S Bel-Bahar <tarikbelbahar at gmail.com>
> To: "Gunseli, E." <e.gunseli at vu.nl>
> Cc: "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
> Date: Thu, 13 Sep 2012 00:51:23 -0700
> Subject: Re: [Eeglablist] On ICA based artifact rejection
>
> I would favor "manually" or otherwise catching/rejecting the noisy epochs
> rather than "saving time", in the interest of more accurate results.
> What guarantees do we have that various things like teeth grinding,
> jaw clenching, brow furrowing, blinking, movement, etc.. is not in the
> data?
>
> There's a plethora of algorithms, automatic methods, and toolboxes to
> clean up EEG data before feeding it to ICA and post-ICA.
> However, in a world of big data, visual inspection is so old-school!
>
> For ICA, the idea is to not confuse ICA with extreme data fluctuations
> that can draw ICA's interest
>
> As far as I understand
> ICA will happily eat and decompose continuous or epoched data, as long as
> there is enough data (e.g., enough time points)
>
>
>
>
>
>
> On Wed, Sep 12, 2012 at 4:02 AM, Gunseli, E. <e.gunseli at vu.nl> wrote:
>
>> Dear all,****
>>
>> ** **
>>
>> I have a question about the steps that should be taken before running ICA.
>> ****
>>
>> ** **
>>
>> If we are going to epoch the data (step 5), why are we manually rejecting
>> the extra noisy parts earlier (step 2). ****
>>
>> Since these extra noisy portions are mostly at beginning and end of trial
>> blocks, they will be gone away during epoching anyway. ****
>>
>> So, epoching the critical time window can save a fair amount of time that
>> else we would have spent on manual inspection.****
>>
>> ** **
>>
>> At this point I have another question; I have read that, it is better to
>> run ICA on continuous, non-epoched data. ****
>>
>> One of the problems of running ICA on epoched data is that, “the baseline
>> correction changes relative values across channels” (S. Luck, ERP Boot Camp
>> Lecture Slides). But probably that is not the only reason to run ICA on
>> continuous data because this problem can easily be overcome via removing
>> the baseline after running ICAs. ****
>>
>> So I guess there should be other problems related to running ICAs on
>> epoched data.****
>>
>> Can anyone provide information about these potential problems?****
>>
>> ** **
>>
>> Kind regards,****
>>
>> Eren****
>>
>> ** **
>>
>> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:
>> eeglablist-bounces at sccn.ucsd.edu] *On Behalf Of *Makoto Miyakoshi
>> *Sent:* Friday, September 07, 2012 00:58
>>
>> *To:* IMALI THANUJA HETTIARACHCHI
>> *Cc:* eeglablist at sccn.ucsd.edu
>> *Subject:* Re: [Eeglablist] On ICA based artifact rejection****
>>
>> ** **
>>
>> 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]****
>>
>> ****
>>
>> ****
>>
>> ****
>>
>>
>> _______________________________________________
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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****
>>
>>
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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>>
>>
>>
>>
>> -- ****
>>
>> 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****
>>
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--
M Nirmala
Ph.D Scholar
Department of Neurophysiology
National Institute of Mental Health and Neurosciences (NIMHANS)
Bangalore - 560029, INDIA
http://nphy.weebly.com/
http://www.linkedin.com/Nirmala<http://www.linkedin.com/profile/edit?trk=hb_tab_pro_top>
+919980162315
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there is always a way!!
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