Alternative myoelectric control through neural synergistic information


  • Patricia Capsi-Morales
  • Deren Barsakcioglu
  • Manuel G. Catalano
  • Giorgio Grioli
  • Antonio Bicchi
  • Dario Farina


This work combines for the first time structural and computational synergies defined by neuronal information. The main idea is to investigate the existence of motor neuron synergies and their potential as sources for myoelectric control. First, we developed a new version of the soft hand with 2 degrees of actuation (DoA) for prosthetic applications. Then, we used HD-sEMG to study the behaviour of motor units in different manipulation tasks and to identify motor modules or neural synergies. Based on this dimensionality reduction in both the mechanical prosthetic design and the neural control, we propose a method to map the neural information into prosthesis control. With this approach, we first show that neural synergies have greater dimensionality than classic muscle synergies. This property and a greater degree of independence determine the possibility of a natural, robust and simultaneous control of several DoA by neural synergies. The proposed method can be implemented into an available framework of online decomposition (i.e. online extraction of motor units) in order to create a platform to study different myoelectric control methods and compare their performance in a virtual environment, and in the real-time control of the SoftHand Pro-2. The creation of this platform permits further developments on the existence of modules in upper-limb motor control, the relation between different synergistic levels and its use for assistive and rehabilitative robotics with different type of patients.




How to Cite

P. Capsi-Morales, D. Barsakcioglu, M. G. Catalano, G. Grioli, A. Bicchi, and D. Farina, “Alternative myoelectric control through neural synergistic information”, MEC Symposium, Aug. 2022.

Conference Proceedings Volume


Myoelectric Control Algorithms