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In this talk I will present an approach for the joint estimation of neural source locations, amplitudes and their interactions (functional connectivity) from magnetoencephalographic (MEG) signals. By formulating a state-space for the source locations and their moments, estimation of functional connectivity is reduced to system identification problem in a non-linear state space with a tractable linear sub-structure, whose solution is derived using a Rao-Blackwellized particle smoother (RBPS) combined with an expectation-maximization (EM) algorithm.
This presentation is part of Minisymposium “MS74 - Sequential Monte Carlo methods for inverse estimation in imaging science”
organized by: Narayan Puthanmadam Subramaniyam (Aalto University) , Sara Sommariva (Aalto University) .