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Empirical Mode Decomposition (EMD) of Neuronal Oscillations

Example waveforms
Example waveform shapes

The Empirical Mode Decomposition (EMD) allows for analyses of oscillatory frequency at a very high temporal resolution, even detecting subtle changes in instantaneous frequency within single cycles of an oscillation. We have shown that the instantaneous frequency profile of a cycle provides a detailed readout of its time-domain waveform shape whilst correcting for any differences in amplitude, timing, or duration between cycles.

We have written an EMD Python-based Toolbox to carry out EMD and Hilbert-Huang Spectral Analyses. This includes new methods we have developed (e.g. to perform waveform shape analysis and to automate EMD masking).
    
References
  1. Andrew J. Quinn, Vítor Lopes-dos-Santos, Norden Huang, Wei-Kuang Liang, Chi-Hung Juan, Jia-Rong Yeh, Anna C. Nobre, David Dupret & Mark W. Woolrich (2021) Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics. Journal of Neurophysiology 10.1152/jn.00201.2021
  2. Andrew J. Quinn, Vitor Lopes-dos-Santos, David Dupret, Anna Christina Nobre & Mark W. Woolrich (2021) EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python. Journal of Open Source Software 10.21105/joss.02977
  3. Marco S. Fabus, Andrew J. Quinn, Catherine E. Warnaby & Mark W. Woolrich (2021)
    Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes. Journal of Neurophysiology 10.1152/jn.00315.2021

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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