[Eeglablist] Choosing block size for Infomax ICA algorithm

[Andreas Widmann]

Hi,

Not really an answer to your question but to my understanding there is at least one misconception:

>  keep the random number generator to a fixed seed, and it seems that the two blocks produce quite different results
If you change block size you do actually reintroduce the randomness you intended to avoid by using a fixed seed. Data are shuffled first. Depending on block length different data enter the block level operations (adjustment of weights, computation of kurtosis etc.) resulting in different final weights. I would, however, not expect any *systematic* effects of increasing or decreasing block size but rather similar effects as if changing the seed (which can indeed be considerable but shouldn’t be systematic). 

Best,
Andreas

> Am 20.09.2018 um 16:51 schrieb 周云晖 <yhzhou17 at fudan.edu.cn>:
> 
> Hi, 
> 
> When I checked the code of runica.m, I found that there are two choices of block size in the algorithm:
> 
> % heuristic default - may need adjustment
> %   for large or tiny data sets!
> DEFAULT_BLOCK        = floor(sqrt(frames/4));  % heuristic default
> DEFAULT_BLOCK          = ceil(min(5*log(frames),0.3*frames)); % heuristic
> 
> and by default the second one is used. I have tried using the first one to a the sample dataset inside EEGLAB, and keep the random number generator to a fixed seed, and it seems that the two blocks produce quite different results. The topography of top 10 major ICs changed, not just different order, or sign flip. Given that rejecting ICs can grossly changed the raw data, I wonder are there any *scientific guide* to choose the block size? Or is it a "dark area" where nobody bothers to explore?
> 
> The comments says different block size may suit different data size, but it is not clear whether I should use larger or smaller blocks for a larger dataset, and what kind of dataset can be considered "large".
> 
> Best,
> 
> Yunhui Zhou
> 
> 
> 
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