[Eeglablist] Problem with channel detection in Run ICA

[Makoto Miyakoshi]

Dear James,

> Linked channels can be overly correlated. electrolyte bridges cause
> neighbouring channels to share the same information resulting in each
> bridged pair decreasing the rank of the data by 1.

This reminded me of a story I heard before that someone once saw a
scalp topo that showed a huge red bar present around from parietal to
occipital due to massive bridging :-)

Makoto

2012/6/26 James Desjardins <jdesjardins at brocku.ca>:
> Hi All,
>
> I have been dealing with this as well.
>
> When the rank of the scalp data is smaller than the number of channels
> it means that there are highly predictable channels in the data (they
> do not have information that could not be derived from other channels
> in the data).
>
> Re-referencing reduces the rank of the data by 1 (e.g. in average
> referenced data each channel is perfectly negatively correlated with
> the average of all other channels).
>
> Interpolating data channels does not increase the rank of the data. If
> you have 128 channels and you interpolate 5 bad channels your rank
> will be 128-5 at best.
>
> Linked channels can be overly correlated. electrolyte bridges cause
> neighbouring channels to share the same information resulting in each
> bridged pair decreasing the rank of the data by 1. I have started
> checking my data for unusually large and invariant correlation
> coefficients across neighbouring sites.
>
>
> --
> James Desjardins, MA
> Technician, Cognitive and Affective Neuroscience Lab
> Department of Psychology, Behavioural Neuroscience
> Brock University
> 500 Glenridge Ave.
> St. Catharines, ON, Canada
> L2S 3A1
> 905-688-5550 x4676
>
>
> Quoting Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
>
>> 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
>>
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>
>
>
>
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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