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

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