Due to the complementary nature of electrical and hemodynamic brain activity, joint data analysis can afford a better understanding of the underlying neural activity estimation. We propose a Bayesian sequential Monte Carlo approach (particle filter, PF), applied to simulated recordings of electrical (EEG) and neurovascular mediated hemodynamic activity (fNIRS/fMRI). The feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined measurements are shown.

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) .

Authors:

Filippo Zappasodi ("G.d'Annunzio" University, Chieti)

Keywords:

bayesian methods