Systematic biases in early ERP and ERF components as a result of high-pass filtering

The event-related potential (ERP) and event-related field (ERF) techniques provide valuable insights into the time course of processes in the brain. Because neural signals are typically weak, researchers commonly filter the data to increase the signal-to-noise ratio. However, filtering may distort the data, leading to false results. Using our own EEG data, we show that acausal high-pass filtering can generate a systematic bias easily leading to misinterpretations of neural activity. In particular, we show that the early ERP component C1 is very sensitive to such effects. Moreover, we found that about half of the papers reporting modulations in the C1 range used a high-pass digital filter cut-off above the recommended maximum of 0.1 Hz. More generally, among 185 relevant ERP/ERF publications, 80 used cutoffs above 0.1 Hz. As a consequence, part of the ERP/ERF literature may need to be re-analyzed. We provide guidelines on how to minimize filtering artifacts.

► We evaluate artifacts due to high-pass filtering used to preprocess ERP/ERF data.
► High-pass filtering can lead to systematic biases between conditions.
► About 40% of surveyed papers used methods that can lead to such biases.
► Guidelines to minimize high-pass filtering artifacts are provided.