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access icon free Wavelets statistical denoising (WaSDe): individual evoked potential extraction by multi-resolution wavelets decomposition and bootstrap

© The Institution of Engineering and Technology
Received 15/09/2017, Accepted 07/12/2018, Revised 18/08/2018, Published 14/01/2019

The present study aims at developing a method to extract single sweep event-related potentials obtained with Eriksen's flanker task. Unlike previous methods, no a priori assumptions on the characteristics of signal and noise are necessary. The method is based on the wavelet decomposition, bootstrap and a statistical determination of the reliable frequency coefficients across the individual signals at each time point: significant coefficients will be conserved, whereas the other ones will be set to zero. After removing the unsystematic coefficients (i.e. the noise), the signal is reconstructed, allowing to keep only the components of the event-related potentials. The performances of the method are evaluated with both simulated data and real event-related potential recordings and compared with other methods.

Inspec keywords: statistical analysis; wavelet transforms; signal denoising; neurophysiology; medical signal processing; signal reconstruction; bioelectric potentials

Other keywords: individual evoked potential extraction; statistical determination; time point; signal reconstruction; wavelet statistical denoising; WaSDe; reliable frequency coefficients; multiresolution wavelet decomposition; single sweep event-related potentials; bootstrap; Eriksen's flanker task; event-related potential recordings; unsystematic coefficients

Subjects: Bioelectric signals; Integral transforms; Biology and medical computing; Electrical activity in neurophysiological processes; Probability theory, stochastic processes, and statistics; Function theory, analysis; Digital signal processing; Signal processing and detection; Integral transforms; Other topics in statistics; Other topics in statistics

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