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bioRxiv · 10.1101/2024.05.23.595598

Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

Madduri, M. M.·Yamagami, M.·Li, S. J.·Burckhardt, S.·Burden, S. A.·Orsborn, A. L.
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Abstract

Neural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework allowed us to characterize user and decoder changes within co-adaptive myoelectric interfaces. Our framework further allowed us to predict how decoder algorithm changes impacted co-adaptive interface performance and revealed how interface properties can shape user behavior. Our findings demonstrate an experimentally-validated computational framework that can be used to design user-decoder interactions in closed-loop, co-adaptive neural interfaces. This framework opens future opportunities to optimize co-adaptive neural interfaces to expand the performance and application domains for neural interfaces.

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Madduri, M. M., Yamagami, M., Li, S. J., Burckhardt, S., Burden, S. A., Orsborn, A. L.. 2024-05-26. Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces. https://doi.org/10.1101/2024.05.23.595598

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