| 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 |