[Eeglablist] AMICA number of mixture components

[Tatu Huovilainen]

Hi Makoto, Dr. Palmer & eeglab list,

I have a few specific questions about AMICA, that I failed to find answers to from previous discussions. What should I use as a criterion for choosing the 'num_mix_comps' parameter? I've understood that increasing the number will result in better model fit, but with a chance of overfitting. Is there a way to make an approximation of how many mixture components it's ok to estimate, like the k(n_channels)^2 rule for infomax? Will it cause trouble (besides taking much longer), given that I have enough samples to avoid overfitting, if a source is well approximated with 3 densities but I'm using, say, 6? Are there other aspects of the data that affect choosing this number, like sensor types or snr?

Regards,
Tatu Huovilainen