[Eeglablist] eeglablist Digest, Vol 95, Issue 21

[Tarik S Bel-Bahar]

If you are just starting to use eeglab, it is a good idea to
try some basic steps with sample data using the eeglab tutorial and
documentation.

For your immediate issue, the answer is easy.
the "Data index" is meant to be
another EEGLAB dataset that you
have in memory, which contains all
the channels. This should be the original
file that has all the original channels.
Then your file with fewer channels
should be easily interpolated, now that you
have a "data index" to point to.

To reiterate, when you load one dataset
into eeglab, that dataset has an index of 1.
When you load a second dataset,
that dataset will have an index of 2.
and so on...







On Sun, Sep 16, 2012 at 9:22 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]****
>>>
>>>  ****
>>>
>>>  ****
>>>
>>>  ****
>>>
>>>
>>> _______________________________________________
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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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