title: Topography-time-frequency atomic decomposition for event-related M/EEG signals.
abstract: We present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation.
authors.affiliation: INSERM U751, Marseille, France. christian.benar@medecine.univ-mrs.fr.
authors.forename: Christian-G
authors.initials: CG
authors.lastname: Bénar
authors.forename: Theodore
authors.initials: T
authors.lastname: Papadopoulo
authors.forename: Maureen
authors.initials: M
authors.lastname: Clerc
mesh_terms: 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; D053798:Magnetocardiography / Q000379:methods*; D008959:Models, Neurological