Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

Journal of Neural Engineering
Pierpaolo CroceAntonio Maria Chiarelli

Abstract

Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstructi...Continue Reading

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Citations

Feb 16, 2018·Journal of Neural Engineering·Antonio Maria ChiarelliFilippo Zappasodi
Sep 3, 2020·International Journal of Molecular Sciences·Mario ForcioneAntonio Belli
Nov 26, 2020·International Journal of Neural Systems·Antonio Maria ChiarelliFilippo Zappasodi
Feb 12, 2021·Frontiers in Human Neuroscience·Haroon KhanPeyman Mirtaheri

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