Proposes a method for rebuild task-related sources. The application proposes electroencephalography (EEG) source imaging model based on temporal graph regularized low-rank representation composed of: (i) data fitting term, (ii) temporal graph embedding regularization term, and (iii); a ℓ1 norm for sparsity penalty and nuclear norm. This model is solved by an algorithm using the alternating direction method of multipliers (ADMM) that is able to extract low-rank task-related source patterns.
Department of Industrial and Manufacturing Systems Engineering University of Texas at Arlington, Arlington, TX, USA; Department of Mathematical Sciences Montana State University, Bozeman, MT, USA; Department of Mathematical Sciences University of Texas at Dallas, Richardson, TX, USA
LiuEtAl2018 funding source(s)
Supported in part by the NSF funding under grant number CMMI-1537504 and DMS-1522786.