Alberto Sorrentino

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We present a Bayesian filtering approach for automatic estimation of dynamical source models from magnetoencephalographic data. We apply multi-target Bayesian filtering and the theory of Random Finite Sets in an algorithm that recovers the life times, locations and strengths of a set of dipolar sources. The reconstructed dipoles are clustered in time and(More)
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Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed(More)
OBJECTIVE To study behavioral and brain responses to variations in signal-to-noise ratio (SNR) of cognitive visual stimuli. METHODS We presented meaningful words visually, embedded in varying amounts of dynamic noise, and utilized magnetoencephalography (MEG) to measure responses to the words. A multidipole model of the evoked fields was constructed to(More)
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