HD-EMG to Assess Motor Learning in Myoelectric Control

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Researchers

  • Sigrid S.G. Dupan
  • Ivan Vujaklija

  • Giulia De Vitis
  • Strahinja S. Dosen
  • Dario Farina
  • Dick F. Stegeman

Research units

  • Radboud University Nijmegen
  • University of Rome La Sapienza
  • Aalborg University
  • Imperial College London

Abstract

Online myoelectric control involves two types of adaptation: computational adaptation, in which the controller learns to associate muscle patterns with performed forces; and behavioural adaptation, where the users learn the new interface, and adapt their motor control strategies based on the errors they observe. In order to study the behavioural motor learning during online myoelectric control, twelve able-bodied participants performed single and 2-finger presses through force and myoelectric control. Myoelectric control was obtained with linear ridge regression, and was based on a training set only containing single finger presses. The distance between muscle patterns of force and EMG control trials indicated that motor learning leads to changes in neural drive, even on the trained presses. This suggests that motor learning is an integral part of myoelectric control, where the ability of the user to learn the EMG-to-force mapping impacts the overall performance of the myoelectric controller.

Details

Original languageEnglish
Title of host publicationConverging Clinical and Engineering Research on Neurorehabilitation III
Subtitle of host publicationProceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), October 16-20, 2018, Pisa, Italy
Publication statusPublished - 1 Jan 2019
MoE publication typeA3 Part of a book or another research book

Publication series

NameBiosystems and Biorobotics
Volume21
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

ID: 31309153