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.

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

“…The visual feedback paradigm for all sessions consisted of the PC domain including the target circle and PC cursor coordinate. Twelve equally spaced targets with radii of 14 percent of the PC domain were set at a radius 70 percent between the origin and the edge of the PC domain and aligned with the vector summation map as described in Radhakrishnan et al's work [30]. Three randomized blocks of twelve targets were presented for each session.…”

“…Pistohl et al controlled individual digits of a virtual hand (VH) and prosthetic hand using intrinsic hand EMG by maneuvering a cursor in a two-dimensional domain, allowing for simultaneous and proportional control of multiple DoFs [29]. Radhakrishnan et al studied users' ability to learn novel myoelectric control interfaces using a two-dimensional center-out target-acquisition task [30]. However, none of these studies focused on the clinical implementationproducing functional postures and using clinically available surface EMG control sites-as we did here using the novel PC algorithm.…”

The Structure and Acquisition of Sensorimotor Maps

“…Previous work has shown that learning is possible under these conditions (Mosier et al, 2005;Liu et al, 2011;Radhakrishnan et al, 2008;Mussa-Ivaldi et al, 2011) but it remains unclear what is the structure of the acquired maps. Initially, participants face two problems: given a target sound they do not know where to move to, and when they make an error, they have no basis on which to correct it.…”

Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands

“…A successful trial consisted of matching the virtual hand to the target posture in 10 s or less (including a 1 s hold time), otherwise the trial was considered a failure. The virtual hand matched the target posture when the coordinate was within the 14 percent radii of the target position in the PC domain and was indicated by the visual interface [24]. The experiment consisted of 60 trials (10 attempts at each target posture).…”