[Eeglablist] Some questions about runica.m function of EEGLAB

[Seyed Mohammad Reza Shahshahni]

Dear all

Looking at the code in the "runica.m" function of EEGLAB, I couldn't
understand some parts.

1- Why is the data broken into blocks and what is the basis for the size of
those blocks?
2- The learning rule is different from the reference papers.
 weights = weights + lrate*(BI+(1-2*y)*u')*weights;

In the papers there is I, not BI. Why has the identity matrix been
multiplied by B?

3- In the sphering or decorrelation of the data, why is cov^(-0.5)
multiplied by 2?
4- What is the role of bias term?
5- What is the Annealing for?
6- How should the covariance matrix be normalized? By N or N-1 ?

Thanks
S. M. R. Shahshahani
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