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

M Browne·T R H Cutmore
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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.

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ArtifactsClinical Trials as TopicComputer SimulationElectricityElectroencephalographyElectromyographyEvoked PotentialsEvoked Potentials, VisualEye MovementsHumansPsychophysiologyReproducibility of ResultsSignal Processing, Computer-AssistedJournal ArticleValidation Study

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BibTeXRIS

M Browne, T R H Cutmore. 2002. Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising.. https://doi.org/10.1016/s1388-2457(02)00194-3

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