Montana State University
Title: Graph Regularizations for the EEG Inverse Problem
Date: Friday, January 25, 2019
Place and Time: Room 101, Love Building, 3:35-4:25 pm
Refreshments: Room 204, Love Building, 3:00 pm
Abstract. Regularization plays an important role in solving inverse problems in a wide spectrum of applications. In particular, regularization techniques for graph-structured datasets have a great potential to revolutionize imaging technologies such as Electroencephalogram (EEG). Estimation of the locations of brain sources from the EEG data, known as source localization, is a challenging ill-posed inverse problem. In this talk, we investigate several EEG source localization methods based on various spatial and temporal graph regularizations, including graph total generalized variation, graph fractional-order total variation, and temporal graph regularization. Numerical results have shown that the proposed methods localize source extents more effectively than the benchmark methods.