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:	Anirudh Wodeyar, Mark Schatza, Alik S Widge, Uri T Eden, Mark A Kramer
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	phase real-time rhythms EEG LFP Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4970fd0485364008b1cfad5f0975b186
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id	oai:doaj.org-article:4970fd0485364008b1cfad5f0975b186
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spelling	oai:doaj.org-article:4970fd0485364008b1cfad5f0975b1862021-11-25T12:35:01ZA state space modeling approach to real-time phase estimation10.7554/eLife.688032050-084Xe68803https://doaj.org/article/4970fd0485364008b1cfad5f0975b1862021-09-01T00:00:00Zhttps://elifesciences.org/articles/68803https://doaj.org/toc/2050-084XBrain 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.Anirudh WodeyarMark SchatzaAlik S WidgeUri T EdenMark A KramereLife Sciences Publications Ltdarticlephasereal-timerhythmsEEGLFPMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	phase real-time rhythms EEG LFP Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	phase real-time rhythms EEG LFP Medicine R Science Q Biology (General) QH301-705.5 Anirudh Wodeyar Mark Schatza Alik S Widge Uri T Eden Mark A Kramer A state space modeling approach to real-time phase estimation
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	article
author	Anirudh Wodeyar Mark Schatza Alik S Widge Uri T Eden Mark A Kramer
author_facet	Anirudh Wodeyar Mark Schatza Alik S Widge Uri T Eden Mark A Kramer
author_sort	Anirudh Wodeyar
title	A state space modeling approach to real-time phase estimation
title_short	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_sort	state space modeling approach to real-time phase estimation
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/4970fd0485364008b1cfad5f0975b186
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A state space modeling approach to real-time phase estimation

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