Optimal filters for ERP research I: A general approach for selecting filter settings

• Published in: Psychophysiology Vol. 61; no. 6; pp. e14531 - n/a
• Main Authors: Zhang, Guanghui, Garrett, David R., Luck, Steven J.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2024
• Subjects: Electroencephalography - methods; ERP amplitude; ERP latency; Evoked Potentials - physiology; Filters; Humans; Latency; scoring methods; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; standardized measurement error; waveform distortion; signal‐to‐noise ratio; standardized measurement error; ERP amplitude; ERP latency; scoring methods; waveform distortion
• ISSN: 0048-5772; 1469-8986; 1469-8986; 1540-5958
• DOI: 10.1111/psyp.14531

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. Filtering can have a large impact on ERP data, influencing both statistical power and the validity of conclusions. However, there is no standardized, widely used method for determining optimal filter settings for cognitive and affective ERP research. Here, we provide a straightforward method along with tools that will allow researchers to easily determine the most appropriate filter settings for their data.