During contact and physical interaction, afferent and efferent information is acquired. Current technologies have shown to have potentials to re-establish the bidirectional communication with the CNS on afferent and efferent pathways through closed-loop interfaces. They combine recording and stimulation capabilities in order to decode neural signals from the CNS or the PNS to actuate neuroelectronic devices and relay the output back to the user in the form of sensory feedback. Emerging evidence suggests that closed-loop interfaces enable volitional control of neuroelectronic devices, and modify patterns of cortical activity through the process of sensory feedback. They can be adopted as interfaces for neural prostheses and assistive robots to replace functions, and for therapeutic devices to rehabilitate functions.

Typical specialized technologies are being developed with the aim to radically improve the “symbiosis” between humans and robots by increasing adaptiveness, robustness, effectiveness, intuitiveness and naturalness, by rapidly becoming part of the body scheme. They resort to several ways of decoding messages from humans and encoding sensory information for recovering the bidirectional communication with both CNS and PNS, with different pros and cons. The central role of the user is a key issue of the design of such devices, posing highly interesting technical challenges, covering issues related to system efficacy, safety in the interaction and user acceptability.

This Research Topic focuses on the new cutting-edge and representative advancements in design, analysis, implementation, and therapeutic testing of human closed-loop interfaces for neuroelectronic devices and assistive robots. Thus, multidisciplinary papers are very encouraged to be submitted. Researchers working in the academic or industrial community are welcomed to submit papers on the peculiar theoretical, technological and experimental aspects associated with the design, development, and the validation of novel closed-loop interfaces.

Keywords: Neuroprostheses, Sensory feedback restoration, Human-robot interaction, Human-Machine Interface, Human-in-the-loop optimization and learning

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