[Eeglablist] How does transferring ICA matrixes between same-subject data sets affect further processing?

Mon Nov 7 19:59:55 PST 2016

Dear Marius,

> Data cleaning and source separation is the toughest frontier in MoBI
research at the moment I'd say,

Signal processing is a secondary issue. What's most important is the *unique
questions* it can address to make a good science. Consider the fact that
designing an experiment has much larger degrees of freedom than the types
of signal processing we can perform (more or less successfully).

> if all components are subtracted, there should again be nothing left, or
where am I on the wrong track here?

You are right.

> Also, the backprojection from components to channels is just a matrix
multiplication with the remaining components' weights inversed, but if
there's no component left, this would be an empty matrix, or how would this
work mathematically? I might have gotten something wrong here, though...

Hmm I have difficulty in seeing your problem. What do you want to know?

Makoto

On Fri, Oct 28, 2016 at 4:55 AM, Marius Klug <marius.s.klug at gmail.com>
wrote:

> Hey Duncan and all!
>
> Yeah, you really should ask Prof. Gramann's group ;-) (Duncan was doing an
> internship with us for a month, I am one of Prof. Gramann's PhD
> students...) We were discussing this at some point, I saw this mail and
> thought "Ah that sounds similar to what Duncan asked me about, let's check
> what the EEGLAB Pros answer!" and only later realized that it was actually
> you asking this question! So, great to have this discussion here!
>
> I'd also say just try out everything and check, this is going to be
> interesting. You know that we're still exploring this ourselves, so well...
> just try out and report to us and the list ;-) We're experiencing heavy
> artifacts of all kinds in our datasets, which is also due to the Phasespace
> Mocap system and the HTC Vive VR headset... Basically, we're in the stage
> of finding suitable solutions for data cleaning and source separation and
> do it similarly, trying out what works best at this point. Evelyn
> Jungnickel just told me that she repeatedly did ICA, then checked where the
> algorithm had troubles separating the sources (e.g. by checking if there
> are time periods in which all the components have strong activity), then
> cleaned those parts out of the original data set and then repeated the
> process until the components appeared comparably clean. ICA and denoising
> is a delicate process for sure... Data cleaning and source separation is
> the toughest frontier in MoBI research at the moment I'd say, and hopefully
> ever more sophisticated solutions will emerge on the research horizon! On
> another note there's also a more general thing "Joint Decorrelation" (
> https://www.ncbi.nlm.nih.gov/pubmed/24990357), which might turn out to be
> useful in some cases. But as I said, we're still on this ourselves... and
> there's also the valid point you raised once that the more you clean the
> data the more signal you are also prone to lose in the process. No perfect
> solutions unfotunately!
>
> As a question from my part: I thought that the ICA components combined
> would always contain the whole original dataset, just split up differently,
> and if you subtract all components you get zero at all channels. I take
> that is true for the data set from which the ICA was calculated? So, if I
> understand this correctly, you say that's not the case any more if you take
> the weights and apply them to longer/other data sets? Really interesting.
> But I'm afraid I don't fully understand this... Makoto you wrote that the
> parts unaccounted for by the ICA would be spread through all components, so
> if all components are subtracted, there should again be nothing left, or
> where am I on the wrong track here? Also, the backprojection from
> components to channels is just a matrix multiplication with the remaining
> components' weights inversed, but if there's no component left, this would
> be an empty matrix, or how would this work mathematically? I might have
> gotten something wrong here, though...
>
> Cheers,
> Marius
>
>
>
> 2016-10-21 15:02 GMT+02:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:
>
>> Hi Duncan, some quick notes about your questions below, hope they are of
>> use. Best wishes, Tarik
>>
>> Mobile brain imaging (EEG in not just "passive and sitting-quietly"
>> conditions) is still a developing field. You may benefit from contacting a
>> few researchers who work in this area, including Klaus Gramman and Daniel
>> Ferris, so as to garner their opinions. There are several other groups that
>> should be findable via google. As you're at the frontier of this area,
>> it's better to test a range of options and make the best informed
>> decision, as there are few consolidated guidelines for this kind of
>> research. There's a range of issues and options, just a few thoughts below
>> for now.
>>
>> If you don't give some subset of data to ICA, it's of course not
>> considering it. If you have several ICA decompositions for one person, then
>> there should definitely be similarities across the decompositions. Thus,
>> it's not clear that you would end up with, after subtractions, just, as you
>> said, "  remaining signal consisting of all the activity that lacked in
>> the passive conditions?". This expectation makes sense, but it's just not
>> published about enough to be sure.
>>
>> And yes, generally, if you are trying to subtract ICs representing neck
>> muscle activity from a dataset that has very little (or a lot of) activity
>> like that, the basic assumptions about subtracting the ICs may not hold.
>>
>> In your case, consider yourself first in an exploratory stage. You are
>> considering several important points, so that's good. The field needs to
>> know more about mobile brain imaging problems and how to deal with them.
>> Overall this is an excellent opportunity to make a contribution to real
>> problems in the field.
>>
>> You should be able to run ICA on the non-passive conditions and get
>> interpretable ICs that reflect real brain sources. However, they will
>> likely be more mixed with artifact activity than the ICs you get from the
>> passive condition.
>>
>> I would recommend running an ICA across all conditions per person, as
>> well as a (separate) ICA for each condition, and review the results for
>> yourself (from single-condition and all-condition ICAs). You may get more
>> "prototypical artifact ICs that are applicable across the conditions. If
>> you have "weak artifact" IC from one condition and apply it to a "receiving
>> set"
>> it should remove some of that artifact activity, but not necessarily most
>> of it, especially if the IC information from the "donor set" does not
>> well-characterize the artifact dynamics in the "receiving set".
>>
>> Another option for future consideration, is to ask people to make a
>>  range of stereotyped movements with their bodies, faces, and arms, and use
>> that as a kind of glossary of artifacts for a specific person.
>>
>> Another option is to compare several conditions, with each condition
>> having "more artifacts". For example, really slow walking, normal walking,
>> and fast walking.
>>
>> Some of your issues like cable sway and so on from movement may be
>> resolved with wireless caps available from several manufacturers, though I
>> would not recommend dry systems nor "non-research" consumer systems.
>>
>> You may also want to think about using ASR in eeglab to clean up the data
>> beforehand. See an example of the effects of ASR on EEG with active
>> movement here:
>> https://www.youtube.com/watch?v=qYC_3SUxE-M
>>
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>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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