  We consider two areas of research that have been developing in parallel over
the last decade: blind source separation (BSS) and electromagnetic source
estimation (ESE). BSS deals with the recovery of source signals when only
mixtures of signals can be obtained from an array of detectors and the only
prior knowledge consists of some information about the nature of the source
signals. On the other hand, ESE utilizes knowledge of the electromagnetic
forward problem to assign source signals to their respective generators, while
information about the signals themselves is typically ignored. We demonstrate
that these two techniques can be derived from the same starting point using the
Bayesian formalism. This suggests a means by which new algorithms can be
developed that utilize as much relevant information as possible. We also
briefly mention some preliminary work that supports the value of integrating
information used by these two techniques and review the kinds of information
that may be useful in addressing the ESE problem.
