title: A maximum linear separation criterion for the analysis of neurophysiological data.
abstract: In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g., total normalized log-power), computed over sets of sites in a discrete domain, such as the time-frequency-topography space. These sets are obtained as those that maximize the linear separation between the two populations, and the corresponding maps provide information that may complement that obtained by standard procedures, such as statistical parametric mapping. It is shown experimentally, using both simulated and real data, that the proposed approach may provide useful information even when the standard procedures fail, due to the conservative nature of the multiple comparison correction that must be applied in the later case.
authors: Center for Research in Mathematics CIMAT, Apartado Postal 402, Guanajuato, Gto. 36000, Mexico. jlm@cimat.mx
authors: Jose L
authors: JL
authors: Marroquin
authors: Omar
authors: O
authors: Mendoza-Montoya
authors: Rolando J
authors: RJ
authors: Biscay
authors: Salvador
authors: S
authors: Ruiz-Correa
authors: Thalia
authors: T
authors: Harmony
authors: Thalia
authors: T
authors: Fernandez
mesh_terms: D000161:Acoustic Stimulation; D000465:Algorithms; D001307:Auditory Perception / Q000502:physiology; D002648:Child; D003198:Computer Simulation; D005072:Evoked Potentials, Auditory / Q000502:physiology; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D007859:Learning Disabilities / Q000503:physiopathology; D008297:Male; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000502:physiology; D013223:Statistics as Topic / Q000379:methods*