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.

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