[Eeglablist] Decimation prior to ICA and related things

[Radu Ranta]

Hello all,

We have perfomed some experiments on the necessary data lenght topic for 
ICA. They are described in a paper we have presented at the Biosignals 
2012 conference a month ago (G. Korats et al, "Impact of window length 
and decorrelation step on ICA algorithms for EEG blind source separation").

Best,
Radu

On 18/03/2012 14:55, Scott Makeig wrote:
> The data_length/channels^2 = k > 30 heuristic was based on observing 
> our 256-channel data decompositions. For fewer channels, I don't think 
> this (k > 30) may be necessary (though a small k will very likely 
> prove a problem!).
>
> But for small numbers of channels, data length is not so much an 
> obstacle -- for example, decomposing 32 channels with k=30 would 
> require only 30*32^2= 32K data points: At a sampling rate of (say) 256 
> Hz, this would be ~2 minutes of data. With 128 channels, the 
> equivalent heuristic would be ~32 minutes.
>
> I hope one of us will be able to do a proper study, decomposing data 
> subsets of various lengths and measuring mutual information reduction 
> and dipolarity (see Delorme et al., PLoS One 2012). Also, Jason Palmer 
> has been working on theoretic lower bounds on ICA accuracy at given 
> data lengths and channel numbers. I'll summarize result with him when 
> possible.
>
> Scott Makeig
>
> On Sat, Mar 17, 2012 at 5:17 PM, Tarik S Bel-Bahar 
> <tarikbelbahar at gmail.com <mailto:tarikbelbahar at gmail.com>> wrote:
>
>     1. not sure if you have to decimate, have seen some people to do
>     this to better meet requirements for good ICA decompositions.
>     2. don't think higher sampling rate will give you better ICA. I
>     think it's more of a matter that ICA is fed data that gives an
>     accurate and lengthy representation of the whole "Data space".
>     3. quote from earlier eeglablist post: "the key factor is how much
>     data you have  (timepoints / channels^2). If this is > 30 (or near
>     to it), then we find it preferable to return all possible
>     components (since pca does a rather poor job of separating sources)."
>     So if below this threshold there is some reason for adding more
>     time to the protocol, or reducing channels, or  decimation
>
>
>
>     On Tue, Mar 13, 2012 at 6:00 AM, Modestino, Edward J *HS
>     <EJM9F at hscmail.mcc.virginia.edu
>     <mailto:EJM9F at hscmail.mcc.virginia.edu>> wrote:
>
>         Dear EEGLAB experts,
>
>         (1) Is it true that  ICA must be subject, like all the
>         model-based spectral analysis methods, to a recommendation
>         that one decimate to the lowest frequency capable of
>         representing the actual signal content of the data without
>         alienation effects? *Does one _NEED_ to decimate the data
>         before running ICA?*  For instance, we have a data set
>         recorded at 1,000 Hz.  Do we need to decimate this to
>         approximately 128 or 256?
>
>         (2 ) According to the formula that Dr. Makeig gave to
>         determine the optimal amount of data,
>         *#timepoints/(#channels)^2*, it would appear that a *higher
>         sampling rate will give better ICA results*.  Is this the case?
>
>         (3) Finally, using this formula, *#timepoints/(#channels)^2*,
>         is there a *threshold or cutoff* one needs to be exceeded to
>         have the optimal amount of data to run ICA. Simply doing the
>         equation without any way to interpret the output is not helpful.
>
>         Thanks for your help,
>
>         Dr. Modestino
>
>         Edward Justin Modestino, Ph.D.
>
>         Postdoctoral Research Associate
>
>         Ray Westphal Neuroimaging Laboratory
>
>         Division of Perceptual Studies
>
>         Department of Psychiatry and Neurobehavioral Sciences
>
>         University of Virginia
>
>         Email: ejm9f at virginia.edu <mailto:ejm9f at virginia.edu>
>
>
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>
>
>
> -- 
> Scott Makeig, Research Scientist and Director, Swartz Center for 
> Computational Neuroscience, Institute for Neural Computation; Prof. of 
> Neurosciences (Adj.), University of California San Diego, La Jolla CA 
> 92093-0559, http://sccn.ucsd.edu/~scott <http://sccn.ucsd.edu/%7Escott>
>
>
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