Bayesian analysis of phase data in EEG and MEG

  1. Sydney Dimmock  Is a corresponding author
  2. Cian O'Donnell
  3. Conor J Houghton
  1. University of Bristol, United Kingdom
  2. University of Ulster, United Kingdom

Abstract

Electroencephalography and magnetoencephalography recordings are non-invasive and temporally precise, making them invaluable tools in the investigation of neural responses in humans. However, these recordings are noisy, both because the neuronal electrodynamics involved produces a muffled signal and because the neuronal processes of interest compete with numerous other processes, from blinking to day-dreaming. One fruitful response to this noisiness has been to use stimuli with a specific frequency and to look for the signal of interest in the response at that frequency. Typically this signal involves measuring the coherence of response phase: here a Bayesian approach to measuring phase coherence is described. This Bayesian approach is illustrated using an example from neurolinguistics and is more descriptive and more data-efficient than the traditional statistical approaches.

Data availability

This manuscript is a computational study, so no data have been generated. All modelling code for this study is available from the GitHub link provided in appendix 2. The statistical learning dataset used as a case study in this paper is not publicly available.

The following previously published data sets were used

Article and author information

Author details

  1. Sydney Dimmock

    Department of Computer Science, University of Bristol, Bristol, United Kingdom
    For correspondence
    sd14814@bristol.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0163-2048
  2. Cian O'Donnell

    School of Computing, Engineering abd Intelligent Systems, University of Ulster, Londonderry, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Conor J Houghton

    Department of Computer Science, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5017-9473

Funding

Leverhulme Trust (RF-2021-533)

  • Conor J Houghton

Medical Research Council (MR/S026630/1)

  • Cian O'Donnell

Engineering and Physical Sciences Research Council (EP/R513179/1)

  • Sydney Dimmock

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Andrea E Martin, Max Planck Institute for Psycholinguistics, Netherlands

Version history

  1. Received: October 31, 2022
  2. Accepted: September 11, 2023
  3. Accepted Manuscript published: September 12, 2023 (version 1)
  4. Accepted Manuscript updated: September 20, 2023 (version 2)

Copyright

© 2023, Dimmock et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Sydney Dimmock
  2. Cian O'Donnell
  3. Conor J Houghton
(2023)
Bayesian analysis of phase data in EEG and MEG
eLife 12:e84602.
https://doi.org/10.7554/eLife.84602

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