[Eeglablist] rank inconsistency problem during ICA when using averaged mastoids reference

Fri Mar 22 17:27:54 PDT 2013

Dear Pete,

>Anyway, sounds like dropping a channel is the best way to go.

Yes, please try it.

Makoto

2013/3/22 Bachman, Peter <bachman at psych.ucla.edu>

>  Thanks Makoto,
>
>  Yep, I've followed the discussion on the listserv; I think it's an
> interesting one.  I guess I was wondering what kind of workarounds people
> used to deal with the issue - to make the dimensions match.  (Sorry, I
> don't think I made that clear in my question.)  Anyway, sounds like
> dropping a channel is the best way to go.
>
>  Thanks,
> Pete
>  ------------------------------
> *From:* Makoto Miyakoshi [mmiyakoshi at ucsd.edu]
> *Sent:* Friday, March 22, 2013 1:22 PM
> *To:* Bachman, Peter
> *Cc:* eeglablist at sccn.ucsd.edu
> *Subject:* Re: [Eeglablist] rank inconsistency problem during ICA when
> using averaged mastoids reference
>
>  Dear Peter,
>
>  It's a well-known topic in this list.
> Actually average referencing reduced the data rank by 1. Do you wonder
> why? It's because you introduce a correlation to the data by subtracting
> the channel average. If you still wonder or want to see mathematical
> explanation, you may wan to search Jason Palmer's post in the list.
>
>  So, you should discard a channel (any channel is fine) to make the
> number of channels matched to the actual rank.
>
>  Makoto
>
> 2013/3/21 Bachman, Peter <bachman at psych.ucla.edu>
>
>>  Hi everyone,
>>
>>  I have a question regarding a rank inconsistency problem that arises
>> when we run ICA on a dataset that has been re-referenced to averaged
>> mastoids.  The problem appears related to the fact that we have
>> re-referenced offline to the average of two channels, and ICA "expects" to
>> find 65 channels (the total number we begin with) but only finds 64.  As
>> a consequence it defaults to using PCA to reduce the number of dimensions -
>> something we'd prefer to avoid with these particular data.
>>
>>  The problem arises regardless of the ICA algorithm we use and also
>> apparently regardless of the re-referencing parameters we use (as long as
>> two reference channels are involved - one channel works fine).  I should
>> also note that the data were recorded in ANT as .cnt files, but I'm
>> guessing that's not critical to this problem.  We're using EEGLAB
>> 11_0_5_4b.
>>
>>  Has anyone else run into this and found a workaround?  Or is there
>> something else we should be doing to ensure that ICA runs, rather than PCA?
>>  (I'm also open to the suggestion that I'm completely misdiagnosing the
>> problem!)
>>
>>  I've pasted a portion of our ICA/PCA output below.  (We're using binica
>> in this example, but the same issue arises with the regular extended
>> infomax algorithm.)
>>
>>  Thanks!
>> Pete
>>
>> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>> Warning: If the binary ICA function does not work, check that you have
>> added the
>> binary file location (in the EEGLAB directory) to your Unix /bin
>> directory (.cshrc file)
>> Warning: fixing rank computation inconsistency (63 vs 64) most likely
>> because running under Linux 64-bit MatlabData rank (64) is smaller than the
>> number of channels (65).
>> binica: using source file
>> '.../matlab/eeglab11_0_5_4b/functions/sigprocfunc/binica.sc'
>> binica(): using binary ica file
>> '?.../matlab/eeglab11_0_5_4b/functions/resources/ica_linux'
>> binica(): processing 4 (flag, arg) pairs.
>>    setting lrate, 0.001
>>    setting pca, 64
>>    setting extended, 1
>> scriptfile = binica8147.sc
>>
>> Running ica from script file binica8147.sc
>>    Finding 64 components.
>> alias erplab '.../data/erplab': Command not found.
>>
>> ICA Version 1.4  (Feb. 14, 2002)
>>
>> Input data size [65,754696] = 65 channels, 754696 frames.
>> After PCA dimension reduction,
>>   finding 64 ICA components using extended ICA.
>> PDF will be calculated initially every 1 blocks using 6000 data points.
>> Initial learning rate will be 0.001, block size 501.
>> Learning rate will be multiplied by 0.98 whenever angledelta >= 60 deg.
>> Training will end when wchange < 1e-07 or after 512 steps.
>> Online bias adjustment will be used.
>>
>> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>>
>>
>>
>> Peter Bachman, PhD
>> Staglin IMHRO Center for Cognitive Neuroscience,
>> Center for the Assessment and Prevention of Prodromal States (CAPPS)
>> & Adolescent Brain and Behavior Research Clinic (ABBRC)
>> Semel Institute for Neuroscience and Human Behavior, UCLA
>> Office: (310) 206-4245
>> bachman at psych.ucla.edu
>>
>>  ------------------------------
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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
>

-- 
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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