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Article Dans Une Revue IET Signal Processing Année : 2019

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

Besma Benchabane
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Moncef Benkherrat
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Salah Djelel
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Aissa Belmeguenai
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Résumé

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.
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Dates et versions

hal-02401454 , version 1 (10-12-2019)

Identifiants

Citer

Besma Benchabane, Moncef Benkherrat, Boris Burle, Franck Vidal, Thierry Hasbroucq, et al.. Wavelets statistical denoising (WaSDe): individual evoked potential extraction by multi-resolution wavelets decomposition and bootstrap. IET Signal Processing, 2019, 13 (3), pp.348-355. ⟨10.1049/iet-spr.2018.5389⟩. ⟨hal-02401454⟩
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