[Eeglablist] Question regarding ICA

[Davide Baldo]

Thanks a lot for your help!

Davide.

On Wed, Sep 26, 2012 at 10:09 PM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com
> wrote:

> My apologies. Sorry to have not been more clear.
> It is my (simple) understanding that ICA *does not care *
> whether the data is epoched or continuous.
> In other words, to ICA, the data looks the same whether it is epoched or
> continuous.
> ICA is basically trying to find spatial patterns/filters that best explain
> the time points you give it.
>
> That being said, there are multiple rules you need to learn about and keep
> in mind
> before "feeding" data to ICA. The data needs to contain enough time points,
> be clean enough, and contain only data from the actual task
> (that is, no periods where "nothing was going on" between experimental
> blocks).
>
> You may want to do some reading on ICA/EEG fundamentals.
> For example, search on Google Scholar for these two items:
>
> 1. ERP features and EEG dynamics: an ICA perspective
> S Makeig, J Onton - Oxford Handbook of Event-Related Potential …, 2011 -
>
> 2. Independent EEG sources are dipolar
> A Delorme, J Palmer, J Onton, R Oostenveld, S Makeig - PloS one, 2012 -
> dx.plos.org
>
> *Please also take the time to search the eeglablist archives and*
> *inform yourself of past conversations. Search on the "keywords" *
> *we have been using, such as epoched, continuous, ICA....*
> For example, I searched on Google for "ICA continuous or epoched"
> and found a few eeglablist conversations, and other pages that
> might help you develop a deeper view of what is going on,
> including the following from the EEGLAB wiki,
> which should be read from start to finish by any new eeglab users.
> *
> *
> * Chapter 09: Decomposing Data Using ICA - SCCN<http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA>
> sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA
> *
>
> The advice from Stephen and others is also important to keep in mind.
>
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> On Wed, Sep 26, 2012 at 1:04 AM, Davide Baldo <davidebaldo84 at gmail.com>
> wrote:
> >
> > Thanks you all for your answer.
> >
> > Now I am wondering what does that mean tha ICA is agnostic about
> continuous/discontinuous data. Could you please explain that concept a bit?
> I am very interested in the topic.
> >
> > Thanks again!
> >
> > Davide.
> >
> >
> > On Wed, Sep 26, 2012 at 1:17 AM, Tarik S Bel-Bahar <
> tarikbelbahar at gmail.com> wrote:
> >>
> >> yes, periods that are not of cognitive interest should be removed,
> >> and ICA is generally agnostic about
> >> whether the data is epoched or continuous.
> >> yes, there have been various conversations on this topic in the list.
> >>
> >> On Fri, Sep 21, 2012 at 4:29 AM, Davide Baldo <davidebaldo84 at gmail.com>
> wrote:
> >>>
> >>> Dear all,
> >>>
> >>> I was wondering about the following problem:
> >>>
> >>> Assume that your experiment has 2 blocks each with 30 Trials (just a
> random numbers). The experiment is divided into 2 blocks in order to give a
> break to the subject at the end of the first block of 30 trials.
> >>> The EEG data during the break will probably be full of any kinds of
> artifacts.
> >>>
> >>> My question is the following: Regarding ICA, what do you do with the
> EEG data recording during the break time? I mean, do you use the complete
> EEG signal to run ICA (including the data recorded during the break time)
> or do you remove the noise data recording during the break?
> >>>
> >>> The point is: if you do not remove that data, then you have a
> continuous signal as input to ICA.
> >>>                    If you do remove that data, you remove a lot of
> noise, but you add a discontinuity in the data you use to run ICA (because
> you must cut the data recorded during the break and join together the
> remaining data)
> >>>
> >>> Thus...is it better to remove that part of the data or not?
> >>>
> >>> Does my question make sense? :)
> >>>
> >>> Thanks in advance for your kind help,
> >>>
> >>>
> >>> Davide.
> >>>
> >>> _______________________________________________
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> >>
> >>
> >
>
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