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 t...
| Main Authors: | , , |
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| Format: | Article |
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Elsevier
2012
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| Online Access: | https://eprints.nottingham.ac.uk/49640/ |
| _version_ | 1848798044101804032 |
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| author | Acunzo, David J. Mackenzie, Graham van Rossum, Mark C.W. |
| author_facet | Acunzo, David J. Mackenzie, Graham van Rossum, Mark C.W. |
| author_sort | Acunzo, David J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:13:30Z |
| format | Article |
| id | nottingham-49640 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:30Z |
| publishDate | 2012 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-496402020-05-04T16:33:29Z https://eprints.nottingham.ac.uk/49640/ 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. 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. Elsevier 2012-07-30 Article PeerReviewed Acunzo, David J., Mackenzie, Graham and van Rossum, Mark C.W. (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. ISSN 1872-678X https://www.sciencedirect.com/science/article/pii/S0165027012002361?via%3Dihub doi:10.1016/j.jneumeth.2012.06.011 doi:10.1016/j.jneumeth.2012.06.011 |
| spellingShingle | Acunzo, David J. Mackenzie, Graham van Rossum, Mark C.W. Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title | Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title_full | Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title_fullStr | Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title_full_unstemmed | Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title_short | Systematic biases in early ERP and ERF components as a result of high-pass filtering |
| title_sort | systematic biases in early erp and erf components as a result of high-pass filtering |
| url | https://eprints.nottingham.ac.uk/49640/ https://eprints.nottingham.ac.uk/49640/ https://eprints.nottingham.ac.uk/49640/ |