Biosignal-based co-adaptive user-machine interfaces for motor control

Abstract

User-machine interfaces map biological signals measured from the user to control commands for external devices. The mapping from biosignals to device inputs is performed by a decoding algorithm. Adaptation of both the user and decoder—co-adaptation—provides opportunities to improve the inclusivity and usability of interfaces for diverse users and applications. User learning leads to robust interface control that can generalize across environments and contexts. Decoder adaptation can personalize interfaces, account for day-to-day signal variability, and improve overall performance. Co-adaptation therefore creates opportunities to shape the user and decoder system to achieve robust and generalizable personalized interfaces. However, co-adaptation creates a two-learner system with dynamic interactions between the user and decoder. Engineering co-adaptive interfaces requires new tools and frameworks to analyze and design user-decoder interactions. In this article, we review adaptive decoding, user learning, and co-adaptation in user-machine interfaces, primarily brain-computer, myoelectric, and kinematic interfaces, for motor control. We then discuss performance criteria for co-adaptive interfaces and propose a game-theoretic approach to designing user-decoder co-adaptation.