题目: Graph Regularizations in EEG Source  Localization

主讲人：秦菁

时间：6月14日(周四)15:00-16:00  

地点：综合楼601会议室

摘要: Electroencephalogram (EEG) serves as an  essential tool for brain source localization due to its high temporal  resolution. However, the inference of brain activities from the EEG data is a  challenging ill-posed inverse problem. In this talk, we investigate several EEG  source localization methods based on various 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. 

个人简介：Jing Qin earned her Ph.D. in applied  mathematics from Case Western Reserve University in 2013, and then worked as an  assistant adjunct professor at the University of California, Los Angeles for  three years. Since summer 2016, she joined the Department of Mathematical  Sciences at Montana State University as an assistant professor. Her research  interests include variational image processing/analysis and its applications,  compressive sensing based image reconstruction, and numerical optimization and  applied partial differential equations.