Bayesian analysis of phase data in EEG and MEG
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.
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Grammatical category and the neural processing of phrases - EEG dataZenodo, doi:10.5281/zenodo.4385970.
Article and author information
Author details
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
- Andrea E Martin, Max Planck Institute for Psycholinguistics, Netherlands
Version history
- Preprint posted: October 17, 2022 (view preprint)
- Received: October 31, 2022
- Accepted: September 11, 2023
- Accepted Manuscript published: September 12, 2023 (version 1)
- Accepted Manuscript updated: September 20, 2023 (version 2)
- Version of Record published: October 20, 2023 (version 3)
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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