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  High-pass filters and baseline correction in M/EEG analysis. Commentary on: “How inappropriate high-pass filters can produce artefacts and incorrect conclusions in ERP studies of language and cognition”

Maess, B., Schröger, E., & Widmann, A. (2016). High-pass filters and baseline correction in M/EEG analysis. Commentary on: “How inappropriate high-pass filters can produce artefacts and incorrect conclusions in ERP studies of language and cognition”. Journal of Neuroscience Methods, 266, 164-165. doi:10.1016/j.jneumeth.2015.12.003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-21B0-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1130-3
Genre: Journal Article

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 Creators:
Maess, Burkhard1, Author              
Schröger, Erich2, Author
Widmann, Andreas2, Author
Affiliations:
1Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205650              
2Department of Cognitive and Biological Psychology, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, Germany, ou_persistent22              

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Free keywords: Baseline correction; High-pass filter; ERP; ERF; EEG; MEG
 Abstract: Tanner et al. (2015. Psychophysiology, 52(8), 1009. doi: 10.1111/psyp.12437) convincingly demonstrate how a late deflection like the N400 or the P600 is reflected into both earlier and later latencies by the application of high-pass filters with cutoff frequencies higher than 0.1 Hz. It nicely underlines the importance of test-wise application of filters with different parameters to electrophysiological data to identify such unwanted filter effects. In general, we agree with their approach and conclusions, particularly with the notions that the application of a high-pass filter is reasonable if it improves the signal-to-noise ratio (SNR) of the signal of interest, and that low frequency signals may carry important information. However, we disagree in two aspects: First, the test data of Tanner et al. are not optimally suited to demonstrate the benefits of high-pass filtering as they are only minimally contaminated by low frequency noise, and second, the standard baseline correction for particular applications in M/EEG data analysis should be replaced with high-pass filtering—as recommended by Widmann et al. (2015. J Neurosci Methods, 250, 46. doi: 10.1016/j.jneumeth.2014.08.002).

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Language(s): eng - English
 Dates: 2015-09-032015-12-112016-01-082016-07-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.jneumeth.2015.12.003
PMID: 26774029
Other: Epub 2016
 Degree: -

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Title: Journal of Neuroscience Methods
  Other : J. Neurosci. Meth.
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 266 Sequence Number: - Start / End Page: 164 - 165 Identifier: ISSN: 0165-0270
CoNE: /journals/resource/954925480594