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

  • 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.

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Metadaten
Author details:Michael RosenblumORCiDGND, Arkady PikovskyORCiDGND, Andrea A. KühnORCiDGND, Johannes Leon BuschORCiD
DOI:https://doi.org/10.1038/s41598-021-97560-5
ISSN:2045-2322
Title of parent work (English):Scientific Reports
Publisher:Springer Nature
Place of publishing:London
Publication type:Article
Language:English
Date of first publication:2021/09/10
Publication year:2021
Release date:2022/05/11
Volume:11
Article number:18037 (2021)
Number of pages:11
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Extern / Extern
DDC classification:5 Naturwissenschaften und Mathematik / 50 Naturwissenschaften / 500 Naturwissenschaften und Mathematik
Peer review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publishing method:Open Access / Gold Open-Access
License (German):License LogoCC BY - Namensnennung, 4.0 International
External remark:Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1241
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