Sequential Decoding of Intramuscular EMG Signals via Estimation of a Markov Model

Jonathan Monsifrot (1) , Eric Le Carpentier (1) , Yannick Aoustin (1) , Darion Farina (2)

1 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
2 InnerEarLab

This paper addresses the sequential decoding of intramuscular single-channel EMG signals to extract the activity of individual motor neurons. A hidden Markov model is derived from the physiological generation of the EMG signal. The EMG signal is described as a sum of several action potentials (wavelet) trains, embedded in noise. For each train, the time interval between wavelets is modeled by a process that parameters are linked to the muscular activity. The parameters of this process are estimated sequentially by a Bayes filter, along with the firing instants. The method was tested on some simulated signals and an experimental one, from which the rates of detection and classification of action potentials were above 95\% with respect to the reference decomposition. The method works sequentially in time, and is the first to address the problem of intramuscular EMG decomposition online. It has potential applications for man-machine interfacing based on motor neuron activities.

Mots clés: Weibull distribution; Bayes methods; Biomedical signal processing; Electromyography; Hidden Markov model; Recursive estimation; Weibull distribution.

Domaines: Robotique [cs.RO]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Ingénierie biomédicale

Jonathan Monsifrot, Eric Le Carpentier, Yannick Aoustin, Darion Farina. Sequential Decoding of Intramuscular EMG Signals via Estimation of a Markov Model. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22 (5), pp.1. ⟨10.1109/TNSRE.2014.2316547⟩. ⟨hal-01024878⟩