EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python

Quinn AJ
Nobre AC
Woolrich M

Scientific 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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Preprint
Causse AA, Curot J, Lopes-Dos-Santos V, Nunes-da-Silva R, Barron HC, Dornier V, Denuelle M, De Barros A, Sol J, Lotterie J, Lehongre K, Fernandez-Vidal S, Frazzini V, Navarro V, Valton L, Barbeau EJ, Denison T, Reddy L, Dupret D

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe

EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python

Quinn AJ
Nobre AC
Woolrich M

Scientific 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.

Citation

Journal of Open Source Software, 6(59), 2977.

DOI

10.21105/joss.02977

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Related Datasets

Similar content

Preprint
Causse AA, Curot J, Lopes-Dos-Santos V, Nunes-da-Silva R, Barron HC, Dornier V, Denuelle M, De Barros A, Sol J, Lotterie J, Lehongre K, Fernandez-Vidal S, Frazzini V, Navarro V, Valton L, Barbeau EJ, Denison T, Reddy L, Dupret D

A learning-evoked slow-oscillatory architecture paces population activity for offline reactivation across the human medial temporal lobe