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)
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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