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

Acunzo, David J.; Mackenzie, Graham; van Rossum, Mark C.W.

Abstract

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.

Citation

Acunzo, D. J., Mackenzie, G., & van Rossum, M. C. (2012). Systematic biases in early ERP and ERF components as a result of high-pass filtering. Journal of Neuroscience Methods, 209(1), https://doi.org/10.1016/j.jneumeth.2012.06.011

• Journal Article Type: Article
• Acceptance Date: Jun 12, 2012
• Online Publication Date: Jun 26, 2012
• Publication Date: Jul 30, 2012
• Deposit Date: Feb 7, 2018
• Publicly Available Date: Feb 7, 2018
• Journal: Journal of Neuroscience Methods
• Print ISSN: 0165-0270
• Electronic ISSN: 1872-678X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 209
• Issue: 1
• DOI: https://doi.org/10.1016/j.jneumeth.2012.06.011
• Public URL: https://nottingham-repository.worktribe.com/output/710587
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0165027012002361?via%3Dihub