Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/20191
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Systematic biases in early ERP and ERF components as a result of high-pass filtering
Author(s): Acunzo, David
MacKenzie, Graham
van Rossum, Mark C W
Contact Email: graham.mackenzie@stir.ac.uk
Keywords: ERP
ERF
High-pass filtering
Data processing
C1
Issue Date: Jul-2012
Date Deposited: 14-May-2014
Citation: Acunzo D, MacKenzie G & van Rossum MCW (2012) Systematic biases in early ERP and ERF components as a result of high-pass filtering. Journal of Neuroscience Methods, 209 (1), pp. 212-218. https://doi.org/10.1016/j.jneumeth.2012.06.011
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.
DOI Link: 10.1016/j.jneumeth.2012.06.011
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