[Eeglablist] ICA decomposition with 128 versus 64 channels?

Mon Jan 23 10:08:44 PST 2006

Ramesh and Joe:

Your comments, while informative, fail to address the point of our paper, 
which is the loss of individual topographic specificity in group (!) data. The 
implications affect any analysis concerned with sources of brain activity.  

Jürgen and Craig

Dept of Biopsychology, New York State Psychiatric Institute
New York, NY 10032
kayserj at pi.cpmc.columbia.edu
tenkecr at pi.cpmc.columbia.edu

On 22 Jan 2006 at 22:39, ramesh wrote:

> CSD -current source density - is an overarching term   for a number of  
> techniques with different physical (and hence physiological meaning).   
> For instance, current source density methods in animal studies are  
> often estimates of current flowing vertically across layers.
> 
> Surface Laplacians used in EEG also give you information of current  
> density (rather than potential).   The current density estimated is the  
> current flowing radially through the skull into the scalp.  We have  
> shown that this is a pretty good estimates of localized (sources  
> smaller than about 3 cm in surface tangential directions) sources in  
> superficial cortex (within 3 cm of the electrodes).  However, if your  
> sources are deeper or are actually over a larger area - say  extending  
> 10 cm in tangential directions, the surface Laplacian will  
> underestimate these sources.
> 
> In any case, surface Laplacians are a way to estimate skull current  
> density and is hence a "CSD" method.  I think CSD is bad terminology in  
> the case of EEG, since the current density is not actually brain  
> "source" current density.
> 
> ramesh
> On Jan 22, 2006, at 9:25 PM, Dien, Joseph wrote:
> 
> > Hey Ramesh!
> >
> > Oops, just so.  :)
> >
> > I'm not very familiar with CSD and Laplacian techniques.  What is the  
> > difference between CSD and surface Laplacians?  Quick version?
> >
> > Joe
> >
> >
> > -----Original Message-----
> > From: ramesh [mailto:srinivar at uci.edu]
> > Sent: Sun 1/22/2006 3:52 PM
> > To: Dien, Joseph
> > Cc: eeglablist at sccn.ucsd.edu; Jürgen Kayser; Matthew Belmonte
> > Subject: Re: [Eeglablist] ICA decomposition with 128 versus 64  
> > channels?
> >
> > Joe - I think you meant high-pass spatial filter and if you look at my
> > new book - http://www.electricfieldsofthebrain Chapter 8, or 1995 paper
> > in Brain Topography, it is only for surface Laplacians (or I suppose
> > source localization) that the higher channel count is helpful.   Indeed
> > if you want to do surface Laplacians there is potentially more
> > information upto an interelectrode spacing of 2 cm, which would require
> > more than 180 electrodes.
> >
> > I suspect for PCA, ICA algorithms the same is true, as outlined by
> > Delome in previous emails to this group.
> >
> > If your just going to look at the EP waveforms or spectra at each
> > channel the move from 64 to 128 is only marginally beneficial.  Again
> > see my book, Chapter 7.
> >
> > ramesj
> > On Jan 21, 2006, at 7:05 PM, Joseph Dien wrote:
> >
> >> I dare say it depends on the intended analysis approach.  CSD is, by
> >> its nature, a low-pass filter.  It therefore makes sense to me that
> >> such an approach might not take advantage of a higher channel count.
> >> Given the Srinivasan et al (1996) and the Fletcher et al (1996)
> >> simulation studies, I'm a little surprised by the findings of this new
> >> study and look forward to reading it.  However, in my experience as
> >> well, for dipole localization procedures more electrodes are better.
> >>
> >> Cheers!
> >>
> >> Joe
> >>
> >>
> >>
> >> On Jan 20, 2006, at 10:56 AM, Jürgen Kayser wrote:
> >>
> >>> Matthew:
> >>>
> >>> You might be interested in looking at the following article, which
> >>> will be
> >>> published in the February 2006 issue of Clinical Neurophysiology
> >>> (currently
> >>> available via the DOI pointer at Elsevier's web site).
> >>>
> >>> Kayser, J., Tenke, C.E. (2006). Principal components analysis of
> >>> Laplacian
> >>> waveforms as a generic method for identifying ERP generator patterns:
> >>> II.
> >>> Adequacy of low-density estimates. Clinical Neurophysiology, 117(2),
> >>> in
> >>> press.
> >>>
> >>> http://dx.doi.org/10.1016/j.clinph.2005.08.033
> >>>
> >>> Our report investigates the benefits of using high- vs. low-density
> >>> EEG
> >>> montages (129 vs. 31 channels) for typical ERP group data, which is
> >>> likely to
> >>> be relevant to your question. Individual topographic specificity of
> >>> ERP
> >>> components derived from high-resolution ERP/CSD data is largely lost
> >>> in
> >>> ERP group data, because averaging across subjects results in a
> >>> spatial low-
> >>> pass filter. If the focus is on brain processes that can be
> >>> generalized to the
> >>> population under study, there seems to be no immediate gain of
> >>> high-density
> >>> recordings. These findings may come as a surprise as they seem to
> >>> contradict common ERP knowledge based on previous recommendations
> >>> using simulated and individual ERP data. Consequently, a (clinical)
> >>> ERP
> >>> researcher would be well-advised to consider the costs and benefits  
> >>> of
> >>> engaging in high-density EEG recordings.
> >>>
> >>> Best, Jürgen and Craig
> >>>
> >>>
> >>>
> >>> On 18 Jan 2006 at 23:41, Matthew Belmonte wrote:
> >>>
> >>>> I'm in the process of putting together a proposal for an EEG
> >>>> facility, and
> >>>> would like to select hardware with EEGLAB processing in mind.  I'm
> >>>> approaching
> >>>> this from perhaps a bit of a dated perspective: in 1996 I was using
> >>>> only 16
> >>>> channels and homebrewed software for time-frequency analysis, and
> >>>> I've spent
> >>>> the intervening decade working exclusively with fMRI.
> >>>>
> >>>> I've heard from one EEGLAB user that 128 channels don't confer much
> >>>> advantage
> >>>> over 64, since inputs must be spatially downsampled in order to be
> >>>> processed
> >>>> practically on typical computing hardware, and since the independent
> >>>> components
> >>>> of interest (those from neural sources) don't become much cleaner
> >>>> with 128
> >>>> inputs as compared to 64.  (The tradeoff of spatial resolution and
> >>>> SNR to
> >>>> electrode application time also is a consideration; we'd be
> >>>> recording from
> >>>> autistic children and couldn't afford any great deal of time spent
> >>>> fiddling.)
> >>>>
> >>>> I'd like to hear from EEGLAB users (and developers!) with experience
> >>>> at 128
> >>>> and/or 64 channels:  Do you find a 64-channel system adequate?  What
> >>>> improvement in data quality has moving to 128 channels given you?
> >>>> If I loaded
> >>>> up a GNU/Linux system with the most RAM that I could get (16GB on an
> >>>> IBM
> >>>> IntelliStation), would it be able to handle an ICA decomposition of
> >>>> 128-channel
> >>>> data without thrashing, or would I be doubling my investment in
> >>>> amplifiers only
> >>>> to have to mix down 128 signals to 64 before ICA?  And, even if it
> >>>> would be
> >>>> computationally practical, would it be scientifically useful enough
> >>>> to justify
> >>>> the extra preparation time?
> >>>>
> >>>> Many thanks
> >>>>
> >>>> Matthew Belmonte <mkb30 at cam.ac.uk>
> >>>> _______________________________________________
> >>>> eeglablist mailing list eeglablist at sccn.ucsd.edu
> >>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> >>>> To unsubscribe, send an empty email to
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> >>>
> >>>
> >>>
> >>> _______________________________________________
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> >>
> >> ---------------------------------------------------------------------- 
> >> -
> >> ---------
> >>
> >> Joseph Dien
> >> Assistant Professor of Psychology
> >> Department of Psychology
> >> 419 Fraser Hall
> >> 1415 Jayhawk Blvd
> >> University of Kansas
> >> Lawrence, KS 66045-7556
> >> E-mail: jdien at ku.edu
> >> Office: 785-864-9822 (note: no voicemail)
> >> Fax: 785-864-5696
> >> http://people.ku.edu/~jdien/Dien.html
> >>
> >>
> >> _______________________________________________
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> >>
> > Ramesh Srinivasan
> > Assistant Professor
> > Department of Cognitive Sciences
> > University of California, Irvine
> > Irvine, CA 92697-5100
> >
> >
> >
> Ramesh Srinivasan
> Assistant Professor
> Department of Cognitive Sciences
> University of California, Irvine
> Irvine, CA 92697-5100
> 