title: Sparse cortical source localization using spatio-temporal atoms.
abstract: This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.
authors.forename: Gundars
authors.initials: G
authors.lastname: Korats
authors.forename: Radu
authors.initials: R
authors.lastname: Ranta
authors.forename: Steven
authors.initials: S
authors.lastname: Le Cam
authors.forename: Valérie
authors.initials: V
authors.lastname: Louis-Dorr
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009146:Music; D059629:Signal-To-Noise Ratio; D062211:Spatio-Temporal Analysis