Zhang Guanghui, Garrett David R, Luck Steven J
Center for Mind & Brain, University of California-Davis, Davis, CA, USA.
bioRxiv. 2023 Dec 12:2023.05.25.542359. doi: 10.1101/2023.05.25.542359.
Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.
滤波在事件相关电位（ERP）研究中起着至关重要的作用，但滤波器设置通常是基于历史先例、实验室经验或非正式分析来选择的。这在一定程度上反映了缺乏一种经过充分论证且易于实施的方法来确定给定类型ERP数据的最佳滤波器设置。为了填补这一空白，我们开发了一种方法，该方法涉及找到能使特定幅度分数的信噪比最大化（或使潜伏期分数的噪声最小化）同时使波形失真最小化的滤波器设置。通过从总体平均ERP波形（通常是差异波形）中获取幅度分数来估计信号。使用单受试者分数的标准化测量误差来估计噪声。通过让无噪声的模拟数据通过滤波器来估计波形失真。这种方法使研究人员能够为其特定的评分方法、实验设计、受试者群体、记录设置和科学问题确定最合适的滤波器设置。我们在ERPLAB Toolbox中提供了一组工具，以便研究人员能够轻松地将这种方法应用于他们自己的数据。
