Learning a novel myoelectric-controlled interface task.

Abstract:

:Control of myoelectric prostheses and brain-machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.

journal_name

J Neurophysiol

authors

Radhakrishnan SM,Baker SN,Jackson A

doi

10.1152/jn.90614.2008

subject

Has Abstract

pub_date

2008-10-01 00:00:00

pages

2397-408

issue

4

eissn

0022-3077

issn

1522-1598

pii

90614.2008

journal_volume

100

pub_type

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