  EEG continues to find a multitude of uses in both neuroscience research and
medical practice, and independent component analysis (ICA) continues to be an
important tool for analyzing EEG. A multitude of ICA algorithms for EEG
decomposition exist, and in the past, their relative effectiveness has been
studied. AMICA is considered the benchmark against which to compare the
performance of other ICA algorithms for EEG decomposition. AMICA exposes many
parameters to the user to allow for precise control of the decomposition.
However, several of the parameters currently tend to be set according to "rules
of thumb" shared in the EEG community. Here, AMICA decompositions are run on
data from a collection of subjects while varying certain key parameters. The
running time and quality of decompositions are analyzed based on two metrics:
Pairwise Mutual Information (PMI) and Mutual Information Reduction (MIR).
Recommendations for selecting starting values for parameters are presented.
