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

Alain de Cheveigné*, Israel Nelken

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

108 Scopus citations

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.

Original languageAmerican English
Pages (from-to)280-293
Number of pages14
JournalNeuron
Volume102
Issue number2
DOIs
StatePublished - 17 Apr 2019

Bibliographical note

Funding Information:
A.d.C. was supported by the EU H2020-ICT grant 644732 (COCOHA) and grants ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL ∗ . I.N. was supported by grant 390/12 from the Israel Science Foundation (ISF) and by ERC advanced grant GA-340063 (project RATLAND). Giovanni Di Liberto offered useful comments on previous manuscripts.

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Fourier analysis
  • causality
  • distortions
  • filter
  • impulse response
  • oscillations
  • ringing
  • time-frequency representation

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