title: Effects of reconstructed magnetic field from sparse noisy boundary measurements on localization of active neural source.
abstract: Localization of active neural source (ANS) from measurements on head surface is vital in magnetoencephalography. As neuron-generated magnetic fields are extremely weak, significant uncertainties caused by stochastic measurement interference complicate its localization. This paper presents a novel computational method based on reconstructed magnetic field from sparse noisy measurements for enhanced ANS localization by suppressing effects of unrelated noise. In this approach, the magnetic flux density (MFD) in the nearby current-free space outside the head is reconstructed from measurements through formulating the infinite series solution of the Laplace's equation, where boundary condition (BC) integrals over the entire measurements provide "smooth" reconstructed MFD with the decrease in unrelated noise. Using a gradient-based method, reconstructed MFDs with good fidelity are selected for enhanced ANS localization. The reconstruction model, spatial interpolation of BC, parametric equivalent current dipole-based inverse estimation algorithm using reconstruction, and gradient-based selection are detailed and validated. The influences of various source depths and measurement signal-to-noise ratio levels on the estimated ANS location are analyzed numerically and compared with a traditional method (where measurements are directly used), and it was demonstrated that gradient-selected high-fidelity reconstructed data can effectively improve the accuracy of ANS localization.
authors.affiliation: State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China.
authors.forename: Hui-min
authors.initials: HM
authors.lastname: Shen
authors.affiliation: Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. kokmeng.lee@me.gatech.edu.|State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China. kokmeng.lee@me.gatech.edu.
authors.forename: Kok-Meng
authors.initials: KM
authors.lastname: Lee
authors.affiliation: State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China. cmeehuli@zju.edu.cn.
authors.forename: Liang
authors.initials: L
authors.lastname: Hu
authors.affiliation: Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, Singapore.
authors.forename: Shaohui
authors.initials: S
authors.lastname: Foong
authors.affiliation: State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Fu
mesh_terms: D006801:Humans; D008280:Magnetics; D008959:Models, Neurological
keywords: Dipole localization; Gradient; Magnetoencephalography; Reconstruction; Spatial interpolation