Bayesian estimation of multiple static dipoles from EEG time series: validation of an SMC sampler

Source modeling of EEG data is an important tool for both neuroscience and clinical applications, such as epilepsy. Despite their simplicity, multiple dipole models remain highly desirable to explain neural sources. However, estimating dipole models from EEG time-series remains a difficult task, mainly due to the ill-posedness of the inverse problem and to the fact that the number of dipoles is usually not known a priori. Recently, a Bayesian approach has been presented for multiple dipole estimation of MEG/EEG data [1,2]: the method estimates simultaneously the number of dipoles and the dipole parameters, by exploring a multiple dipole state space with a Monte Carlo procedure combined with a tempering schedule [3]. Here, we present the first validation of this method with experimental EEG data.