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

Please use this identifier to cite or link to this item: https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/808

Title:	EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python
Authors:	External author(s) only
Keywords:	Brain Activity
Issue Date:	Mar-2021
Citation:	Andrew J. Quinn, Vitor Lopes-dos-Santos , David Dupret , Anna Christina Nobre, and Mark W. Woolrich. EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python. Journal of Open Source Software 6(59), 2977. 31 March 2021
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.
Description:	This work is licensed under a Creative Commons Attribution 4.0 International License.
URI:	https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/808
Appears in Collections:	Neuroscience

Files in This Item:

There are no files associated with this item.

DSpace JSPUI