[Eeglablist] eeglablist Digest, Vol 95, Issue 21
nirmala m
nimmims at gmail.com
Tue Sep 18 02:23:08 PDT 2012
Thanks a lot to all for ur valuable information..i wil try those methods
then keep u informed....once again thanks...
On Tue, Sep 18, 2012 at 8:41 AM, Tarik S Bel-Bahar
<tarikbelbahar at gmail.com>wrote:
> 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:
>>
>>> Send eeglablist mailing list submissions to
>>> eeglablist at sccn.ucsd.edu
>>>
>>> To subscribe or unsubscribe via the World Wide Web, visit
>>> http://sccn.ucsd.edu/mailman/listinfo/eeglablist
>>> or, via email, send a message with subject or body 'help' to
>>> eeglablist-request at sccn.ucsd.edu
>>>
>>> You can reach the person managing the list at
>>> eeglablist-owner at sccn.ucsd.edu
>>>
>>> When replying, please edit your Subject line so it is more specific
>>> than "Re: Contents of eeglablist digest..."
>>>
>>> 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
>>>> 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***
>>>> *
>>>>
>>>> _______________________________________________
>>>> 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
>>>>
>>>
>>>
>>> _______________________________________________
>>> eeglablist mailing list eeglablist at sccn.ucsd.edu
>>> Eeglablist page: http://www.sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to eeglablist-unsub at sccn.ucsd.edu
>>> To switch to non-digest mode, send an empty email to
>>> eeglablist-nodigest at sccn.ucsd.edu
>>>
>>
>>
>>
>> --
>> 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*
>>
>>
>> _______________________________________________
>> 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
>>
>
>
--
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*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20120918/1371e26b/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/20120918/1371e26b/attachment.jpg>
More information about the eeglablist
mailing list