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

Quinn AJ
Lopes-Dos-Santos V
Dupret D
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.

A local field potential plotted and the top in black, and plots of six extracted 'intrinctic mode functions' (IMFs) which are oscillatory, each at a different frequency, with amplitude for each changing throughout the plotted period. The bottom plot, IMF 6, theta, is a large and fairly constant amplitude.
A segment of the LFP recording separated into six intrinsic mode functions using a masked empirical mode decomposition. The theta oscillation is isolated into IMF 6.
Citation
Journal of Open Source Software, 6(59), 2977.
DOI
10.21105/joss.02977
Full Text PDF
380.39 KB
Related Datasets
Quinn AJ, Lopes-dos-Santos V, Nobre AC, Dupret D, Woolrich MW
Instantaneous frequency profiles of theta cycles
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