For Electromagnetic Brain Imaging, Robust Empirical Bayesian Reconstruction of Distributed Sources PROJECT TITLE : Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging ABSTRACT: Electromagnetic brain imaging uses non-invasive recordings of magnetic fields and electric potentials to reconstruct brain activity. The estimation of the quantity, location, and time course of sources, particularly for the reconstruction of scattered brain sources with complicated spatial extent, is an ongoing issue in this imaging modality. Kernel smoothing and hyperparameter tiling are two of the major principles in this new robust empirical Bayesian technique that can better reconstruct scattered brain source activity. Smooth Champagne is the name given to the proposed algorithm since it incorporates many of the performance features of the sparse source reconstruction technique Champagne. Smooth Champagne is able to withstand high amounts of noise, interference, and strongly linked brain activity. Smooth Champagne outperforms benchmark techniques in detecting the geographical extent of scattered source activity in simulations. Also, the MEG and EEG data are accurately reconstructed using Smooth Champagne. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Fusing Multi-Level CNN Features to Detect RGB-T Salient Objects For a Discrete MumfordÎÜShah Model, Semi-Linearized Proximal Alternating Minimization is used.
