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

• Published in: Clinical neurophysiology 2002-09, Vol.113 (9), p.1403-1411
• Main Authors: Browne, M., Cutmore, T.R.H.
• Format: Article
• Language: English
• Subjects: Behavioral psychophysiology; Biological and medical sciences; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; Denoising; Electricity - adverse effects; Electroencephalography - methods; Electroencephalography - standards; Electromyography - methods; Electromyography - standards; Electrophysiology; Event-related potential; Evoked Potentials - physiology; Evoked Potentials, Visual - physiology; Eye Movements - physiology; Filtering; Fundamental and applied biological sciences. Psychology; Humans; Pre-processing; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychophysiology; Psychophysiology - methods; Reproducibility of Results; Signal Processing, Computer-Assisted; Wavelet
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/S1388-2457(02)00194-3

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.