[Eeglablist] Question about Order of rereferencing and artifact removal

[Makoto Miyakoshi]

Dear Steven,

>Is considered better to remove artifactual data before rereferencing to
the average, or before filtering the data?

I believe so. If you have a dead channel, or even worse, a zombie channel
removed from the skin and continuously generating high-amplitude noise, you
don't want to include them in the average referencing.

>In addition, when we tried to rereference to the average immediately after
removing artifactual data, when we got to the point where we tried to run
ICA, we got an error message that read:

Average referencing reduces the data rank by 1. This is why. You should
discard a channel (any channel is ok) after average referencing. I've just
replied to the same question, so you may want to check it too.

Makoto

2013/3/21 Steven Pillen <stevendpillen at gmail.com>

> Hello, EEGLABList.
>
> There was a question we had at our lab about the order of operations when
> it comes to rereferencing and other steps of preprocessing.  Is considered
> better to remove artifactual data before rereferencing to the average, or
> before filtering the data?
>
> In addition, when we tried to rereference to the average immediately after
> removing artifactual data, when we got to the point where we tried to run
> ICA, we got an error message that read:
>
> "EEGLAB has detectat that the rank of your data matrix is lower the number
> [sic] of input data channels.  This might be because you are including a
> reference channel or because you are running a second ICA decomposition.
>  The proposed dimension for ICA is 31 (out of 32 channels). Rank
> computation may be innaccurate so you may edit this number below.  If you
> do not understand, simply press OK."
>
> It would not run with either 31, or 32 channels, and we're not sure why.
>
> Does anyone have any insight as to why?
>
> Thank you,
> Steven Pillen
>
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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
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