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  Digital filter design for electrophysiological data: A practical approach

Widmann, A., Schröger, E., & Maess, B. (2015). Digital filter design for electrophysiological data: A practical approach. Journal of Neuroscience Methods, 250, 34-46. doi:10.1016/j.jneumeth.2014.08.002.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-75EE-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-14FA-D
Genre: Journal Article

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 Creators:
Widmann, Andreas1, Author
Schröger, Erich1, Author
Maess, Burkhard2, Author              
Affiliations:
1Department of Cognitive and Biological Psychology, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, Germany, ou_persistent22              
2Methods 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              

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Free keywords: Filtering; Filter distortions; Filter parameters; Preprocessing; Electrophysiology
 Abstract: Background: Filtering is a ubiquitous step in the preprocessing of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Besides the intended effect of the attenuation of signal components considered as noise, filtering can also result in various unintended adverse filter effects (distortions such as smoothing) and filter artifacts. Method: We give some practical guidelines for the evaluation of filter responses (impulse and frequency response) and the selection of filter types (high-pass/low-pass/band-pass/band-stop; finite/infinite impulse response, FIR/IIR) and filter parameters (cutoff frequencies, filter order and roll-off, ripple, delay and causality) to optimize signal-to-noise ratio and avoid or reduce signal distortions for selected electrophysiological applications. Results: Various filter implementations in common electrophysiology software packages are introduced and discussed. Resulting filter responses are compared and evaluated. Conclusion: We present strategies for recognizing common adverse filter effects and filter artifacts and demonstrate them in practical examples. Best practices and recommendations for the selection and reporting of filter parameters, limitations, and alternatives to filtering are discussed.

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Language(s): eng - English
 Dates: 2014-05-302014-08-012014-08-132015-07-30
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.jneumeth.2014.08.002
PMID: 25128257
Other: Epub 2014
 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: 250 Sequence Number: - Start / End Page: 34 - 46 Identifier: ISSN: 0165-0270
CoNE: /journals/resource/954925480594