# [Eeglablist] Problem with channel detection in Run ICA

Remi, Jan Dr. Jan.Remi at med.uni-muenchen.de
Tue Jun 26 00:49:39 PDT 2012

```Dear Jason,
thank you for the swift answer.
I do believe it is the high frequency filter that does the trick. We have to use a high-frequency filter because of the fMRI artifacts, so we use 80Hz.
I don't have any channels at 0, so I guess it is that channels are too similar, even though I don't believe that they are exactly the same. This is very helpful, because it essentially means that I am not loosing any data unintentionally.
Thank you very much, and thank you also for the great tutorial website, I especially enjoyed your video-taped lectures, Jason.
One more quick question: On the website (matlab wiki) it says that there will be a matlab-course in san diego in December 2012. Is that still a valid information? I will be in San Diego in early Decmber for the American Epilepsy Society Meeting, and would consider extending my stay if there was a matlab course.

Best regards from Munich,
Jan Rémi
________________________________________
Von: Jason Palmer [japalmer29 at gmail.com]
Gesendet: Dienstag, 26. Juni 2012 06:07
An: mmiyakoshi at ucsd.edu; Remi, Jan Dr.
Cc: eeglablist at sccn.ucsd.edu; catarinaduarte86 at gmail.com
Betreff: RE: [Eeglablist] Problem with channel detection in Run ICA

Hi Jan,

The data rank is roughly the number of unique time series contained in the
collection of data channels. If the data is not "full rank" (rank less than
channels) then some channels are essentially just linear combinations of
other channels.  E.g. if two channels are exactly the same, then the data
will have rank reduced by at least 1. Similarly if one channel is zero (or
you keep the reference channel after re-referencing.)

Your raw data should have rank equal to the number of channels. If you then
do average reference, the rank should be reduced by 1.

Further reduction is rank is almost certainly due to low-pass filtering.
Low-pass filtering removes high-frequency "details" from the channels. If
channels only differ significantly in high-frequency details, then the
output the low-pass filter applied to each may be the same.

The number of dimensions in the data after filtering will depend on the
specific data recordings (it will generally be different for different
participants, even if all other conditions are the same). It will also
depend on the "severity" of the low-pass filter--a  lower frequency cut-off
will generally remove more high-frequency details and thus produce data with
lower rank.

Best,
Jason

-----Original Message-----
From: Makoto Miyakoshi [mailto:mmiyakoshi at ucsd.edu]
Sent: Monday, June 25, 2012 1:47 PM
To: Remi, Jan Dr.; Jason Palmer
Cc: eeglablist at sccn.ucsd.edu; catarinaduarte86 at gmail.com
Subject: Re: [Eeglablist] Problem with channel detection in Run ICA

Dear Jason,

In short, Jan is asking why he sometimes has different ranks though having
the same number of channels. I'm interested in this question too. I

Makoto

2012/6/21 Remi, Jan Dr. <Jan.Remi at med.uni-muenchen.de>:
> Dear EEGLAB users,
>
> I am using EEGLAB to run an ICA on my EEG data that I acquire in an
> EEG-fMRI environment to ultimately get rid of the cardioballistogram
> artifact that is typical for recording EEG inside the strong magnet of an
MRI machine.
>
> Recently I get a message that reads as follows:
> "EEGLAB has detected that the rank of your data matrix is lower [than]
> the number 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 57 (out of 62
> channels). Rank computation may be inaccurate so you may edit this
> number below. If you do not understand, simply press OK below."
>
> Besides being very thankful for the last sentence, I really do not
> understand the problem. Actually the number of channels that EEGLAB
> proposes varies between 57 and 60 (out of the actual 62 channels) for
> the 6 files I want to run the ICA on. These files differ only in the
> stimulus condition, the EEG properties are not changed at all, they
> are recorded on the same EEG machine (Neuroscan Maglink), with the
> exact same setup for approximately the same time (about 9:45 minutes
> each). So while I of course do expect the EEG to differ in some
> properties of the EEG signal, i.e. changes in gamma band etc., the
> recording setup conditions are the same. So I do not see where there
> would be a systematic mistake in the recording, especially since I
> have had the same failure notice on a data set, where I had used the
> ICA before without any problem and then 2 weeks later, when I wanted
> to redo the ICA on the same EEG data, where I had only applied a
> different filter in the Neuroscan software before running the ICA analysis
(a different low frequency filter), I get the same failure notice.
> More over, the channels that are not displayed in the channel
> selection dialog before running the ICA is not systematic, once it was
> for example the EEG channel F5, once the EEG channel P7.
>
> The ICA itself gets me great decomposition, I can get rid of the
> artifact very nicely, I am happy with the resulting data, but I don't
> like the idea, that I am possibly systematically missing data. I do
> read the EEG in a clinical way, I am a medical researcher.
>
> Any ideas where my mistake could be?
> A similar question had been asked in 2011 and 2009, mainly pertaining
> to a problem of displaying all channels in a 32 bit dataset.
>
> In case you need screenshots of my problem I will be happy to answer
> emails to my email-adress directly.
>
> Thank you all, I enjoy EEGLAB and its community a lot,
>
> Jan Rémi
> Epilepsy and Sleep Center, Department of Neurology, University of
> Munich
> currently: Department of Neurology, University of Coimbra, Portugal
>
>
>
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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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