Brain imaging with magneto/electro-encephalography
from source localization to connectivity estimation
07 Feb 2022, 15:00 — Room 705, UniGe DIBRIS, Via Dodecaneso 35, Genoa, Italy.
![Alberto_Sorrentino_ur6ltd - [Capispalla Braccio Barba]](/media/images/Alberto_Sorrentino_ur6ltd_kvfl54_pRLxv7R.width-800.jpg)
Speaker:
Alberto Sorrentino — Università di Genova
Alberto Sorrentino — Università di Genova
Abstract:
Electroencephalography and magnetoencephalography (EEG / MEG) are two powerful techniques that record signals from the brain with a resolution of one millisecond. EEG / MEG data are very rich in information and are currently used in many contexts, ranging from basic neuroscience to clinical applications. In this paper I will consider two intertwined problems related to the analysis of EEG / MEG data: (1) the problem of localization of the source, which is equivalent to estimating the neural currents underlying the recorded time series; (2) the problem of estimating connectivity, which is equivalent to characterizing the interactions between areas spatially distinct areas.
Electroencephalography and magnetoencephalography (EEG / MEG) are two powerful techniques that record signals from the brain with a resolution of one millisecond. EEG / MEG data are very rich in information and are currently used in many contexts, ranging from basic neuroscience to clinical applications. In this paper I will consider two intertwined problems related to the analysis of EEG / MEG data: (1) the problem of localization of the source, which is equivalent to estimating the neural currents underlying the recorded time series; (2) the problem of estimating connectivity, which is equivalent to characterizing the interactions between areas spatially distinct areas.
Bio:
Alberto Sorrentino is Associate Professor of Numerical Analysis at the Department of Mathematics (DIMA) of the University of Genoa, Italy, and CEO of Bayesian Estimation for Engineering Solutions, a startup recently founded as a spinoff of the University. His research focuses on the development of Bayesian approaches and Monte Carlo methods, with applications in neuroimaging and air quality monitoring.
Alberto Sorrentino is Associate Professor of Numerical Analysis at the Department of Mathematics (DIMA) of the University of Genoa, Italy, and CEO of Bayesian Estimation for Engineering Solutions, a startup recently founded as a spinoff of the University. His research focuses on the development of Bayesian approaches and Monte Carlo methods, with applications in neuroimaging and air quality monitoring.