Filters: When, Why, and How (Not) to Use Them

Alain de Cheveigné; Israel Nelken

doi:10.1016/j.neuron.2019.02.039

Filters: When, Why, and How (Not) to Use Them

Neuron. 2019 Apr 17;102(2):280-293. doi: 10.1016/j.neuron.2019.02.039.

Authors

Alain de Cheveigné¹, Israel Nelken²

Affiliations

¹ Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, Paris, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL, Paris, France; UCL Ear Institute, London, UK. Electronic address: alain.de.cheveigne@ens.fr.
² Edmond and Lily Safra Center for Brain Sciences and the Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel. Electronic address: israel.nelken@mail.huji.ac.il.

PMID: 30998899
DOI: 10.1016/j.neuron.2019.02.039

Abstract

Filters are commonly used to reduce noise and improve data quality. Filter theory is part of a scientist's training, yet the impact of filters on interpreting data is not always fully appreciated. This paper reviews the issue and explains what a filter is, what problems are to be expected when using them, how to choose the right filter, and how to avoid filtering by using alternative tools. Time-frequency analysis shares some of the same problems that filters have, particularly in the case of wavelet transforms. We recommend reporting filter characteristics with sufficient details, including a plot of the impulse or step response as an inset.

Keywords: Fourier analysis; causality; distortions; filter; impulse response; oscillations; ringing; time-frequency representation.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Artifacts*
Causality
Data Accuracy*
Fourier Analysis
Humans
Neurosciences
Signal Processing, Computer-Assisted*
Signal-To-Noise Ratio*
Wavelet Analysis