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

Abstract

Neural interfaces map biological signals measured from a user to control commands for external devices. The mapping from biosignals to device inputs is performed by the decoder. Adaptation of both the user and decoder—co-adaptation—provides opportunities to improve the accessibility and usability of interfaces across diverse users and applications. User learning leads to robust interface control that can generalize across environments and contexts. Decoder adaptation can individualize interfaces and account for signal variability. 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 achieve stable user-decoder interactions. This thesis aims to develop and experimentally validate methods for designing and measuring co-adaptive interfaces. I present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. I tested these computational methods using an experimental platform where human participants learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces. The overarching aim of this thesis is to propose co-adaptive analysis and tools that can predictably influence co-adaptive interface performance and user-decoder dynamics. These findings from this thesis contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces.