title: Multireference adaptive noise canceling applied to the EEG.
abstract: The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroeancephalogram (EEG), with the adaptation implemented by means of a multilayer-perception artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.
authors.affiliation: Department of Electrical and Electronic Engineering, University of Canterbury, Christchurch, New Zealand.
authors.forename: C J
authors.initials: CJ
authors.lastname: James
authors.forename: M T
authors.initials: MT
authors.lastname: Hagan
authors.forename: R D
authors.initials: RD
authors.lastname: Jones
authors.forename: P J
authors.initials: PJ
authors.lastname: Bones
authors.forename: G J
authors.initials: GJ
authors.lastname: Carroll
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted