title: EEG/MEG source localization using source deflated matching pursuit.
abstract: A matching pursuit (MP) based algorithm, called source deflated matching pursuit (SDMP), is proposed for locating sources of brain activity. By iteratively deflating the contribution of identified sources to multiple measurement vectors (MMVs), the SDMP algorithm transforms the original multi-basis-vector/matrix selection problem into a single-basis-vector/matrix selection problem, which not only mitigates the residual-source interference but also remedies the intrinsic bias when locating deep sources. The robustness of the proposed algorithm to two bias factors is verified through simulations.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA. scwu@uci.edu
authors.forename: Shun Chi
authors.initials: SC
authors.lastname: Wu
authors.forename: A Lee
authors.initials: AL
authors.lastname: Swindlehurst
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D003201:Computers; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D012984:Software