Noise Reduction in Rhythmic and Multitrial Biosignals With Applications to Event-Related Potentials

• Published in: IEEE transactions on biomedical engineering 2008-07, Vol.55 (7), p.1809-1821
• Main Authors: Celka $^{\ast}$, Patrick, Le, Khoa N., Cutmore, Timothy R. H.
• Format: Article
• Language: English
• Subjects: Additive noise; Algorithms; Australia; Brain - physiology; Electroencephalography - methods; Enterprise resource planning; Event-related potentials (ERPs); Evoked Potentials - physiology; Fluctuations; Humans; Noise reduction; Principal component analysis; principal component analysis (PCA); Principal components analysis; rhythmic patterns; Sensitivity and Specificity; Signal processing algorithms; Signal to noise ratio; Studies; visual oddball paradigm; wavelet; Wavelet analysis; Wavelet transforms
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2008.919851

A new noise reduction algorithm is presented for signals displaying repeated patterns or multiple trials. Each pattern is stored in a matrix, forming a set of events, which is termed multievent signal. Each event is considered as an affine transform of a basic template signal that allows for time scaling and shifting. Wavelet transforms, decimated and undecimated, are applied to each event. Noise reduction on the set of coefficients of the transformed events is applied using either wavelet de- noising or principal component analysis (PCA) noise reduction methodologies. The method does not require any manual selection of coefficients. Nonstationary multievent synthetic signals are employed to demonstrate the performance of the method using normalized mean square error against classical wavelet and PCA based algorithms. The new method shows a significant improvement in low SNRs (typically