Co-Adaptive Myoelectric Interface for Continuous Control*

Neural interfaces provide novel opportunities for augmenting human capabilities in domains like human-machine interaction, brain-computer interfaces, and rehabilitation. However, the performance of these interfaces varies significantly across users. Decoders that adapt to individual users have the potential to reduce variability and improve performance but introduce a “two-learner” problem as the user simultaneously adapts to the changing decoder. We propose and experimentally test a game-theoretic framework to optimize closed-loop performance of a myoelectric interface for continuous control (based on surface electromyography, sEMG) through co-adaptation of the user and decoder. Human subjects learned to use our interface to perform a two-dimensional trajectory-tracking task. Closed-loop performance was affected by decoder learning rate but not by initialization or decoder cost weights. Our study indicates the potential for co-adaptation in humans and machines to optimize the performance of neural interfaces.