[Eeglablist] eeglablist Digest, Vol 95, Issue 21
Stephen Politzer-Ahles
politzerahless at gmail.com
Mon Sep 17 19:53:18 PDT 2012
Hello Nirmala,
If you know the index of the bad electrode that you want to interpret, just
run the following command from the command line:
EEG = eeg_interp( EEG, [bad_electrode] );
replacing "bad_electrode" with the number(s) of the electrode(s).
Best,
Steve Politzer-Ahles
On Sun, Sep 16, 2012 at 11:22 PM, nirmala m <nimmims at gmail.com> wrote:
> 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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>> Today's Topics:
>>
>> 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]****
>>>
>>> ****
>>>
>>> ****
>>>
>>> ****
>>>
>>>
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
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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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>>
>>
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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
> !!Where there is a will
> there is always a way!!
> P *Save trees. Do not print this mail unless absolutely required **Save
> Earth*
>
>
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--
Stephen Politzer-Ahles
University of Kansas
Linguistics Department
http://www.linguistics.ku.edu/
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