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

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DC Field	Value	Language
dc.contributor.author	External author(s) only	-
dc.date.accessioned	2021-05-14T10:16:59Z	-
dc.date.available	2021-05-14T10:16:59Z	-
dc.date.issued	2021-03	-
dc.identifier.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	en
dc.identifier.uri	https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/808	-
dc.description	This work is licensed under a Creative Commons Attribution 4.0 International License.	en
dc.description.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.	en
dc.description.sponsorship	Supported by the NIHR	en
dc.description.uri	https://doi.org/10.21105/joss.02977	en
dc.language.iso	en	en
dc.subject	Brain Activity	en
dc.title	EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python	en
dc.type	Article	en
Appears in Collections:	Neuroscience

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