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Updated: Oct 24, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
Optimizing learning for control of human-machine interfaces
Srikantan S Nagarajan1, Preeya Khanna2
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Abstract:
Human-machine interfaces, such as augmented reality devices, hand prostheses, and brain-computer interfaces, are increasingly being used in neurorehabilitation. In this issue of Neuron, Vassiliadis et al.1 examine the interaction between closed-loop continuous reinforcement and sensory feedback in learning of human-machine interface control.
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