Approaches based on nonlinear Kalman filtering to deal with physiological noise in the estimation of the hemodynamic response from functional near-infrared spectroscopy data

This thesis proposes a novel algorithm based on nonlinear Kalman filtering to reduce physiological noise that contaminates functional near-infrared spectroscopy (fNIRS) signals and improve the estimation of the hemodynamic response. fNIRS is a recent neuroimaging technique which can monitor brain activity non invasively by using near-infrared light. In particular, comparisons with other literature methods will be carried out, highlighting the strength of the model-based method proposed