Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering.

This paper describes a sequential decomposition algorithm for single-channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. As in previous work, we establish a hidden Markov model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated with the state vector of the hidden Markov model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90%. The recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its real-time applications in human-machine interfaces. The proposed method substantially broadens the domains of applicability of the algorithm.