title: Solving of L0 norm constrained EEG inverse problem.
abstract: l(0) norm is an effective constraint used to solve EEG inverse problem for a sparse solution. However, due to the discontinuous and un-differentiable properties, it is an open issue to solve the l(0) norm constrained problem, which is usually instead solved by using some alternative functions like l(1) norm to approximate l(0) norm. In this paper, a continuous and differentiable function having the same form as the transfer function of Butterworth low-pass filter is introduced to approximate l(0) norm constraint involved in EEG inverse problem. The new approximation based approach was compared with l(1) norm and LORETA solutions on a realistic head model using simulated sources. The preliminary results show that this alternative approximation to l(0) norm is promising for the estimation of EEG sources with sparse distribution.
authors: Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, ChengDu, Sichuan, 610054, China.
authors: Peng
authors: P
authors: Xu
authors: Xu
authors: X
authors: Lei
authors: Xiao
authors: X
authors: Hu
authors: Dezhong
authors: D
authors: Yao
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity