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

[Bachman, Peter]

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<mailto:bachman at psych.ucla.edu>

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