Real-time estimation of phase and amplitude with application to neural data

Abstract Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal’s past an...

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Autores principales:	Michael Rosenblum, Arkady Pikovsky, Andrea A. Kühn, Johannes L. Busch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1645c001fef7442aa6d1b35eec35d85e
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spelling	oai:doaj.org-article:1645c001fef7442aa6d1b35eec35d85e2021-12-02T17:19:17ZReal-time estimation of phase and amplitude with application to neural data10.1038/s41598-021-97560-52045-2322https://doaj.org/article/1645c001fef7442aa6d1b35eec35d85e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97560-5https://doaj.org/toc/2045-2322Abstract Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal’s past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient’s beta-band brain activity.Michael RosenblumArkady PikovskyAndrea A. KühnJohannes L. BuschNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael Rosenblum Arkady Pikovsky Andrea A. Kühn Johannes L. Busch Real-time estimation of phase and amplitude with application to neural data
description	Abstract Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal’s past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient’s beta-band brain activity.
format	article
author	Michael Rosenblum Arkady Pikovsky Andrea A. Kühn Johannes L. Busch
author_facet	Michael Rosenblum Arkady Pikovsky Andrea A. Kühn Johannes L. Busch
author_sort	Michael Rosenblum
title	Real-time estimation of phase and amplitude with application to neural data
title_short	Real-time estimation of phase and amplitude with application to neural data
title_full	Real-time estimation of phase and amplitude with application to neural data
title_fullStr	Real-time estimation of phase and amplitude with application to neural data
title_full_unstemmed	Real-time estimation of phase and amplitude with application to neural data
title_sort	real-time estimation of phase and amplitude with application to neural data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1645c001fef7442aa6d1b35eec35d85e
work_keys_str_mv	AT michaelrosenblum realtimeestimationofphaseandamplitudewithapplicationtoneuraldata AT arkadypikovsky realtimeestimationofphaseandamplitudewithapplicationtoneuraldata AT andreaakuhn realtimeestimationofphaseandamplitudewithapplicationtoneuraldata AT johanneslbusch realtimeestimationofphaseandamplitudewithapplicationtoneuraldata
_version_	1718381011404849152

Real-time estimation of phase and amplitude with application to neural data

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