Highly accurate real-time decomposition of single channel intramuscular EMG(minor revison) - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Biomedical Engineering Year : 2022

Highly accurate real-time decomposition of single channel intramuscular EMG(minor revison)


Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved. Methods: In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPUimplemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed. In this paper, a substantially extended version of this algorithm that boosts the accuracy while maintaining real-time implementation, is introduced. Specifically, multiple heuristics that aim at resolving the problems leading to performance degradation, are applied to the original model. In addition, the recursive maximum likelihood (RML) estimator previously used to estimate the statistical parameters of the spike trains, is replaced by a linear regression (LR) estimator, which is computationally more efficient, in order to ensure real-time decomposition with the new heuristics. Results: The algorithm was validated using twenty-one experimental iEMG signals acquired from the tibialis anterior muscle of five subjects by fine wire electrodes. All signals were decomposed in real time. The decomposition accuracy depended on the level of muscle activation and was >90% when less than 10 MUs were identified, substantially exceeding previous real-time results. Conclusion: Single channel iEMG signals can be very accurately decomposed in real time with the proposed algorithm. Significance: The proposed highly accurate algorithm for singlechannel iEMG decomposition has the potential of providing neural information on motor tasks for human interfacing.
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hal-03544788 , version 1 (26-01-2022)


  • HAL Id : hal-03544788 , version 1


Tianyi Yu, Konstantin Akhmadeev, Eric Le Carpentier, Yannick Aoustin, Dario Farina. Highly accurate real-time decomposition of single channel intramuscular EMG(minor revison). IEEE Transactions on Biomedical Engineering, 2022, 69 (2), pp.746-756. ⟨hal-03544788⟩
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