title: ARX filtering of single-sweep movement-related brain macropotentials in mono- and multi-channel recordings.
abstract: A technique of stochastic parametric identification and filtering is applied to the analysis of single-sweep event-related potentials. This procedure, called AutoRegressive with n eXogenous inputs (ARXn), models the recorded signal as the sum of n+1 signals: the background EEG activity, modeled as an autoregressive process driven by white noise, and n signals, one of which represents a filtered version of a reference signal carrying the average information contained in each sweep. The other (n-1) signals could represent various sources of noise (i.e., artifacts, EOG, etc.). An evaluation of the effects of both artifact suppression and accurate selection of the average signal on mono- or multi-channel scalp recordings is presented.
authors.affiliation: Università di Roma La Sapienza, Rome, Italy.
authors.forename: L
authors.initials: L
authors.lastname: Capitanio
authors.forename: G C
authors.initials: GC
authors.lastname: Filligoi
authors.forename: D
authors.initials: D
authors.lastname: Liberati
authors.forename: S
authors.initials: S
authors.lastname: Cerutti
authors.forename: F
authors.initials: F
authors.lastname: Babiloni
authors.forename: L
authors.initials: L
authors.lastname: Fattorini
authors.forename: A
authors.initials: A
authors.lastname: Urbano
mesh_terms: D001931:Brain Mapping; D003626:Data Display; D004569:Electroencephalography; D004585:Electrooculography; D005071:Evoked Potentials; D006801:Humans; D008959:Models, Neurological; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes