Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, Paris, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL, Paris, France; UCL Ear Institute, London, UK.
Edmond and Lily Safra Center for Brain Sciences and the Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel.
Neuron. 2019 Apr 17;102(2):280-293. doi: 10.1016/j.neuron.2019.02.039.
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.
滤波器常用于降低噪声并提高数据质量。滤波器理论是科学家培训的一部分，但滤波器对解释数据的影响并不总是被充分认识到。本文回顾了这个问题，解释了滤波器是什么，使用滤波器时会遇到哪些问题，如何选择合适的滤波器，以及如何通过使用替代工具来避免滤波。时频分析与滤波器具有一些相同的问题，特别是在小波变换的情况下。我们建议以足够的细节报告滤波器特性，包括作为插图的脉冲或阶跃响应的图。
