Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

High-pass filtering artifacts in multivariate classification of neural time series data

View ORCID ProfileJoram van Driel, View ORCID ProfileChristian N.L. Olivers, View ORCID ProfileJohannes J. Fahrenfort
doi: https://doi.org/10.1101/530220
Joram van Driel
Institute for Brain and Behaviour Amsterdam, Department of Experimental and Applied Psychology - Cognitive Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Joram van Driel
Christian N.L. Olivers
Institute for Brain and Behaviour Amsterdam, Department of Experimental and Applied Psychology - Cognitive Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christian N.L. Olivers
Johannes J. Fahrenfort
Institute for Brain and Behaviour Amsterdam, Department of Experimental and Applied Psychology - Cognitive Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Johannes J. Fahrenfort
  • For correspondence: fahrenfort.work@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

0. Abstract

Background Traditionally, EEG/MEG data are high-pass filtered and baseline-corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional time-series analysis have been well-documented, including temporal displacements. However, its effects on time-resolved multivariate pattern classification analyses (MVPA) are largely unknown.

New Method To prevent potential displacement effects, we extend an alternative method of removing slow drift noise – robust detrending – with a procedure in which we mask out all cortical events from each trial. We refer to this method as trial-masked robust detrending.

Results In both real and simulated EEG data of a working memory experiment, we show that both high-pass filtering and standard robust detrending create artifacts that result in the displacement of multivariate patterns into activity silent periods, particularly apparent in temporal generalization analyses, and especially in combination with baseline correction. We show that trial-masked robust detrending is free from such displacements.

Comparison with Existing Method(s) Temporal displacement may emerge even with modest filter cut-off settings such as 0.05 Hz, and even in regular robust detrending. However, trial-masked robust detrending results in artifact-free decoding without displacements. Baseline correction may unwittingly obfuscate spurious decoding effects and displace them to the rest of the trial.

Conclusions Decoding analyses benefits from trial-masked robust detrending, without the unwanted side effects introduced by filtering or regular robust detrending. However, for sufficiently clean data sets and sufficiently strong signals, no filtering or detrending at all may work adequately. Implications for other types of data are discussed, followed by a number of recommendations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Author note: CNLO and JJF share senior authorship. JvD and CNLO designed the experiment. JvD and JJF designed and conducted the simulations and analyses. CNLO, JvD and JJF wrote the paper. This work was supported by ERC-CoG-2013-615423 grant from the European Research Council, and NWO Vici grant 453-16-002 both awarded to CNLO.

  • Minor revision, some minor changing of wording, added some references, added a figure.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted January 13, 2021.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
High-pass filtering artifacts in multivariate classification of neural time series data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
High-pass filtering artifacts in multivariate classification of neural time series data
Joram van Driel, Christian N.L. Olivers, Johannes J. Fahrenfort
bioRxiv 530220; doi: https://doi.org/10.1101/530220
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
High-pass filtering artifacts in multivariate classification of neural time series data
Joram van Driel, Christian N.L. Olivers, Johannes J. Fahrenfort
bioRxiv 530220; doi: https://doi.org/10.1101/530220

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4079)
  • Biochemistry (8751)
  • Bioengineering (6467)
  • Bioinformatics (23315)
  • Biophysics (11720)
  • Cancer Biology (9135)
  • Cell Biology (13227)
  • Clinical Trials (138)
  • Developmental Biology (7404)
  • Ecology (11360)
  • Epidemiology (2066)
  • Evolutionary Biology (15078)
  • Genetics (10390)
  • Genomics (14001)
  • Immunology (9110)
  • Microbiology (22026)
  • Molecular Biology (8773)
  • Neuroscience (47318)
  • Paleontology (350)
  • Pathology (1419)
  • Pharmacology and Toxicology (2480)
  • Physiology (3701)
  • Plant Biology (8044)
  • Scientific Communication and Education (1427)
  • Synthetic Biology (2206)
  • Systems Biology (6009)
  • Zoology (1247)