Convergent Bayesian formulations of blind source separation and electromagnetic source estimation
This work provides a theoretical unification for researchers in signal processing and neuroscience, though it is incremental as it builds on existing Bayesian methods.
The paper tackles the parallel development of blind source separation (BSS) and electromagnetic source estimation (ESE) by demonstrating that both techniques can be derived from the same Bayesian starting point, suggesting a way to develop new algorithms that integrate more relevant information.
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.