Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising

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Title Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising
Author Browne, Matthew; Cutmore, Timothy
Journal Name Clinical Neurophysiology
Editor Dr. P.M. Rossini, Dr. M. Hallett
Year Published 2002
Place of publication Ireland
Publisher Elsevier
Abstract Objectives: The aim of this paper is to introduce and test a general, wavelet-based method for the automatic removal of noise and artefact from psychophysiological data. Methods: Statistical wavelet thresholding (SWT) performs blind source separation by transforming data to the wavelet domain, and subsequent filtering of wavelet coefficients based on a statistical framework. The observed wavelet coefficients are modelled using a Gaussian distribution, from which low-probability outliers are attenuated based on their z-scores. Results: The technique was applied to both simulated and real event-related potentials (ERP) data. SWT applied to artificial data displayed increased signal-to-noise ratio (SNR) improvements as noise amplitude increased. ERP averages of filtered experimental data displayed a correlation of 0.93 with operator-filtered data, compared with a correlation of 0.56 for unfiltered data. The energy of operator-designated contaminated trials was attenuated by a factor of 7.46 relative to uncontaminated trials. SNR improvement was observed in simulated tests. Conclusions: Variations of SWT may be useful in situations where one wishes to separate uncommon/uncharacteristic structures from time series data sets. For artefact removal applications, SWT appears to be a valid alternative to expert operator screening.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/S1388-2457(02)00194-3
Volume 113
Page from 1403
Page to 1411
ISSN 1388-2457
Date Accessioned 2003-04-08
Date Available 2015-05-04T22:04:36Z
Language en_US
Research Centre Behavioural Basis of Health; Menzies Health Institute Qld
Faculty Griffith Health Faculty
Subject PRE2009-Biological Psychology (Neuropsychology, Psychopharmacology, Physiologica; PRE2009-Neurocognitive Patterns and Neural Networks
URI http://hdl.handle.net/10072/6815
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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