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A state space modeling approach to real-time phase estimation
Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of pha...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536256/ https://www.ncbi.nlm.nih.gov/pubmed/34569936 http://dx.doi.org/10.7554/eLife.68803 |
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author | Wodeyar, Anirudh Schatza, Mark Widge, Alik S Eden, Uri T Kramer, Mark A |
author_facet | Wodeyar, Anirudh Schatza, Mark Widge, Alik S Eden, Uri T Kramer, Mark A |
author_sort | Wodeyar, Anirudh |
collection | PubMed |
description | Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments. |
format | Online Article Text |
id | pubmed-8536256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-85362562021-10-25 A state space modeling approach to real-time phase estimation Wodeyar, Anirudh Schatza, Mark Widge, Alik S Eden, Uri T Kramer, Mark A eLife Neuroscience Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments. eLife Sciences Publications, Ltd 2021-09-27 /pmc/articles/PMC8536256/ /pubmed/34569936 http://dx.doi.org/10.7554/eLife.68803 Text en © 2021, Wodeyar et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Wodeyar, Anirudh Schatza, Mark Widge, Alik S Eden, Uri T Kramer, Mark A A state space modeling approach to real-time phase estimation |
title | A state space modeling approach to real-time phase estimation |
title_full | A state space modeling approach to real-time phase estimation |
title_fullStr | A state space modeling approach to real-time phase estimation |
title_full_unstemmed | A state space modeling approach to real-time phase estimation |
title_short | A state space modeling approach to real-time phase estimation |
title_sort | state space modeling approach to real-time phase estimation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536256/ https://www.ncbi.nlm.nih.gov/pubmed/34569936 http://dx.doi.org/10.7554/eLife.68803 |
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