Myoelectric control with abstract decoders

The objective of this study was to compare the use of muscles appropriate for partial-hand prostheses with those typically used for complete hand devices and to determine whether differences in their underlying neural substrates translate to different levels of myoelectric control.

We developed a novel abstract myoelectric decoder based on motor learning. Three muscle pairs, namely, an intrinsic and independent, an intrinsic and synergist and finally, an extrinsic and antagonist, were tested during abstract myoelectric control. Feedback conditions probed the roles of feed-forward and feedback mechanisms.

Both performance levels and rates of improvement were significantly higher for intrinsic hand muscles relative to muscles of the forearm. Intrinsic hand muscles showed considerable improvement generalising to decoder use without visual feedback. Results indicate that visual feedback from the decoder is used for transitioning between muscle activity levels, but not for maintaining state. Both individual and group performance were found to be strongly related to motor variability.

Physiological differences inherent to the hand muscles can translate to improved prosthesis control. Our results support the use of motor learning based techniques for upper-limb myoelectric control and strongly argues for their utility in control of partial-hand prostheses. We provide evidence of myoelectric control skill acquisition and offer a formal definition for abstract decoding in the context of prosthetic control.