Bayesian analysis of phase data in EEG and MEG
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-09-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/84602 |
| _version_ | 1797654406631522304 |
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| author | Sydney Dimmock Cian O'Donnell Conor Houghton |
| author_facet | Sydney Dimmock Cian O'Donnell Conor Houghton |
| author_sort | Sydney Dimmock |
| collection | DOAJ |
| description | 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 two examples from neurolinguistics and its properties are explored using simulated data. We suggest that the Bayesian approach is more descriptive than traditional statistical approaches because it provides an explicit, interpretable generative model of how the data arises. It is also more data-efficient: it detects stimulus-related differences for smaller participant numbers than the standard approach. |
| first_indexed | 2024-03-11T16:59:04Z |
| format | Article |
| id | doaj.art-2c9ee025b8524fb1916b44f3083e1948 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-11T16:59:04Z |
| publishDate | 2023-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-2c9ee025b8524fb1916b44f3083e19482023-10-20T12:39:21ZengeLife Sciences Publications LtdeLife2050-084X2023-09-011210.7554/eLife.84602Bayesian analysis of phase data in EEG and MEGSydney Dimmock0https://orcid.org/0000-0002-0163-2048Cian O'Donnell1https://orcid.org/0000-0003-2031-9177Conor Houghton2https://orcid.org/0000-0001-5017-9473Faculty of Engineering, University of Bristol, Bristol, United KingdomFaculty of Engineering, University of Bristol, Bristol, United Kingdom; School of Computing, Engineering & Intelligent Systems, Ulster University, Derry/Londonderry, United KingdomFaculty of Engineering, University of Bristol, Bristol, United KingdomElectroencephalography 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 two examples from neurolinguistics and its properties are explored using simulated data. We suggest that the Bayesian approach is more descriptive than traditional statistical approaches because it provides an explicit, interpretable generative model of how the data arises. It is also more data-efficient: it detects stimulus-related differences for smaller participant numbers than the standard approach.https://elifesciences.org/articles/84602EEGMEGBayesiancircular statisticsneurolinguisticsfrequency-tagging |
| spellingShingle | Sydney Dimmock Cian O'Donnell Conor Houghton Bayesian analysis of phase data in EEG and MEG eLife EEG MEG Bayesian circular statistics neurolinguistics frequency-tagging |
| title | Bayesian analysis of phase data in EEG and MEG |
| title_full | Bayesian analysis of phase data in EEG and MEG |
| title_fullStr | Bayesian analysis of phase data in EEG and MEG |
| title_full_unstemmed | Bayesian analysis of phase data in EEG and MEG |
| title_short | Bayesian analysis of phase data in EEG and MEG |
| title_sort | bayesian analysis of phase data in eeg and meg |
| topic | EEG MEG Bayesian circular statistics neurolinguistics frequency-tagging |
| url | https://elifesciences.org/articles/84602 |
| work_keys_str_mv | AT sydneydimmock bayesiananalysisofphasedataineegandmeg AT cianodonnell bayesiananalysisofphasedataineegandmeg AT conorhoughton bayesiananalysisofphasedataineegandmeg |