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dc.contributor.authorExternal author(s) only
dc.identifier.citationAndrew 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 2021en
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en
dc.description.abstractThe 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.sponsorshipSupported by the NIHRen
dc.subjectBrain Activityen
dc.titleEMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Pythonen

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