Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (UMR5287)

Aquitaine Institute for Cognitive and Integrative Neuroscience



INCIA - UMR 5287- CNRS
Université de Bordeaux

Zone nord Bat 2 2ème étage
146, rue Léo Saignat
33076 Bordeaux cedex
France

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CNRS Ecole Pratique des Hautes Etudes Université de Bordeaux

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Home > Latest publications

Couraud M, Cattaert D, Paclet F, Oudeyer PY, de Rugy A (2017)

by Loïc Grattier - published on

Model and experiments to optimize co-adaptation in a simplified myoelectric control system. Journal of Neural Engineering. doi: 10.1088/1741-2552/aa87cf.

Abstract:
OBJECTIVE: To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. APPROACH: We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. RESULTS: First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. SIGNIFICANCE: The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional myoelectric contexts are discussed.