  Independent component analysis (ICA), is a blind source separation method
that is becoming increasingly used to separate brain and non-brain related
activities in electroencephalographic (EEG) and other electrophysiological
recordings. It can be used to extract effective brain source activities and
estimate their cortical source areas, and is commonly used in machine learning
applications to classify EEG artifacts. Previously, we compared results of
decomposing 13 71-channel scalp EEG datasets using 22 ICA and other blind
source separation (BSS) algorithms. We are now making this framework available
to the scientific community and, in the process of its release are testing a
recent ICA algorithm (Picard) not included in the previous assay. Our test
framework uses three main metrics to assess BSS performance: Pairwise Mutual
Information (PMI) between scalp channel pairs; PMI remaining between component
pairs after decomposition; and, the complete (not pairwise) Mutual Information
Reduction (MIR) produced by each algorithm. We also measure the "dipolarity" of
the scalp projection maps for the decomposed component, defined by the number
of components whose scalp projection maps nearly match the projection of a
single equivalent dipole. Within this framework, Picard performed similarly to
Infomax ICA. This is not surprising since Picard is a type of Infomax algorithm
that uses the L-BFGS method for faster convergence, in contrast to Infomax and
Extended Infomax (runica) which use gradient descent. Our results show that
Picard performs similarly to Infomax and, likewise, better than other BSS
algorithms, excepting the more computationally complex AMICA. We have released
the source code of our framework and the test data through GitHub.
