Bayesian multi-dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

We deal with estimation of multiple dipoles from combined MEG and EEG time-series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the number of dipoles and their locations. By considering three test cases, we show that using the combined data the method can localize sources that are not easily (or not at all) visible with either of the two individual data alone. In addition, the posterior distribution from combined data exhibits a lower variance, i.e. lower uncertainty, than the posterior from single device.