[Eeglablist] Problem with channel detection in Run ICA

Tue Jun 26 09:29:55 PDT 2012

Dear Jan,

> We have to use a high-frequency filter because of the fMRI artifacts, so we use 80Hz.

Then you must have subtracted gradient switching artifact and also
ballistocardiogram before ICA. The average-artifact subtraction could
have affected the rank more than the low-pass filter more than the 80
Hz low-pass filter, since 80 Hz should not be terribly low.

Makoto

2012/6/26 Remi, Jan Dr. <Jan.Remi at med.uni-muenchen.de>:
> 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.
>
> Hope that’s helpful.
>
> 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
> appreciate your help.
>
> 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
>

-- 
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego