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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...

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Autores principales: Wodeyar, Anirudh, Schatza, Mark, Widge, Alik S, Eden, Uri T, Kramer, Mark A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
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.
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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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