Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration

IEEE Trans Biomed Eng. 2012 Mar;59(3):645-52. doi: 10.1109/TBME.2011.2177662. Epub 2011 Nov 29.


Pattern recognition of myoelectric signals for prosthesis control has been extensively studied in research settings and is close to clinical implementation. These systems are capable of intuitively controlling the next generation of dexterous prosthetic hands. However, pattern recognition systems perform poorly in the presence of electrode shift, defined as movement of surface electrodes with respect to the underlying muscles. This paper focused on investigating the optimal interelectrode distance, channel configuration, and electromyography feature sets for myoelectric pattern recognition in the presence of electrode shift. Increasing interelectrode distance from 2 to 4 cm improved pattern recognition system performance in terms of classification error and controllability (p < 0.01). Additionally, for a constant number of channels, an electrode configuration that included electrodes oriented both longitudinally and perpendicularly with respect to muscle fibers improved robustness in the presence of electrode shift (p < 0.05). We investigated the effect of the number of recording channels with and without electrode shift and found that four to six channels were sufficient for pattern recognition control. Finally, we investigated different feature sets for pattern recognition control using a linear discriminant analysis classifier and found that an autoregressive set significantly (p < 0.01) reduced sensitivity to electrode shift compared to a traditional time-domain feature set.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Arm / physiology
  • Artificial Limbs*
  • Electrodes
  • Electromyography / instrumentation*
  • Electromyography / methods
  • Humans
  • Muscle Fibers, Skeletal / physiology
  • Pattern Recognition, Automated / methods*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted