EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python

Andrew J Quinn; Vitor Lopes-Dos-Santos; David Dupret; Anna Christina Nobre; Mark W Woolrich

doi:10.21105/joss.02977

EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python

Andrew J Quinn, Vitor Lopes-Dos-Santos, David Dupret, Anna Christina Nobre, Mark W Woolrich

Psychology

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Abstract

The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature analysis. These implementations are supported by online documentation containing a range of practical tutorials.

Original language	English
Article number	2977
Number of pages	6
Journal	The Journal of Open Source Software
Volume	6
Issue number	59
DOIs	https://doi.org/10.21105/joss.02977
Publication status	Published - 31 Mar 2021

Keywords

EMD
time-series
nonlinear
dynamics

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10.21105/joss.02977Licence: Creative Commons: Attribution (CC BY)

QuinnA2021EMDFinal published version, 380 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python",

abstract = "The Empirical Mode Decomposition (EMD) package contains Python (>=3.5) functions for analysis of non-linear and non-stationary oscillatory time series. EMD implements a family of sifting algorithms, instantaneous frequency transformations, power spectrum construction and single-cycle feature analysis. These implementations are supported by online documentation containing a range of practical tutorials.",

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AU - Dupret, David

AU - Nobre, Anna Christina

AU - Woolrich, Mark W

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KW - dynamics

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EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python

Abstract

Keywords

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