Characterizing Expectation Mismatch in a Brain-Controlled Upper-Body Rehabilitation Exoskeleton

Abstract

Robot-assisted therapy has long promised to advance stroke rehabilitation by delivering intensive and personalized training, yet its clinical impact remains limited. Closing the sensorimotor loop with brain-computer interfaces offers a better strategy than passive mobilization, directly linking user intent to robotic assistance and potentially driving neuroplasticity. However, a brain-computer interface requires subject-specific calibration that is time-consuming and often impractical. Moreover, brain decoding remains error-prone due to variability of neural signals, thus resulting in unintended robot actions that could reduce engagement during closed-loop control. Here, we demonstrate that a decoder trained on an expert subject can be transferred to naïve users for online control of a rehabilitation exoskeleton in a rest-versus-reaching paradigm, a functional task with clinical relevance. We then characterize error-related potentials arising from expectation mismatches between brain commands and robot actions during closed-loop control. Finally, we show that these mismatches can be reliably decoded in a subject-independent framework (mean area under the receiver operating characteristic curve: 0.77), a crucial step toward rehabilitation scenarios where collecting subject-specific error-related potential data is challenging. Our findings highlight the potential for integrating real-time error-detection to enhance human-robot interaction by correcting unintended robot behaviors, which could significantly improve rehabilitation outcomes where accurate and contingent feedback is essential.