PiPo.Bayesfilter: Bayesian Filtering for EMG Envelope Extraction

February 12, 2014 #EMG #Max #MuBu #PiPo

I developed a new PiPo for Max6 dedicated to EMG envelope extraction. The object is based on a non-linear Bayesian filtering method that has 2 amazing properties: stability and reactivity. It addresses the limitations of low-pass filtering techniques that suffer from a smoothing of rapid changes in the EMG signal. Here,

the filtered signal is modeled as a combined diffusion and jump process, and the measured EMG is modeled as a random process with a density in the exponential family. […] This estimate yields results with very low short-time variation but also with the capability of rapid response to change. [Sanger2007].

Pipo EMG Screenshot

The method is implemented as a PiPo object for Max6, and is part of the current MuBu release, freely available at Ircam Forum:


Pipo EMG Screenshot

Thanks a lot to David Hoffmann for guiding me through this EMG bibliography!

References

  • TD Sanger, “Bayesian filtering of myoelectric signals,” Journal of neurophysiology, vol. 97, no. 2, 2007, pp. 1839--1845.
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