Human motor augmentation with an extra robotic arm without functional interference.

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions.

Decoding bladder state from pudendal intraneural signals in pigs.

Neuroprosthetic devices used for the treatment of lower urinary tract dysfunction, such as incontinence or urinary retention, apply a pre-set continuous, open-loop stimulation paradigm, which can cause voiding dysfunctions due to neural adaptation. In the literature, conditional, closed-loop stimulation paradigms have been shown to increase bladder capacity and voiding efficacy compared to continuous stimulation. Current limitations to the implementation of the closed-loop stimulation paradigm include the lack of robust and real-time decoding strategies for the bladder fullness state.

Surgical treatment of recurrent retroperitoneal sarcoma in its different patterns: A 15-years' two-centers experience.

Introduction: There is limited data available regarding the role of surgery in the treatment of retroperitoneal sarcoma (RPS) recurrences. We herein report the short- and mid-term outcomes of patients who underwent surgical treatment of RPS recurrences at two Italian centers over a 15-years' experience.

Materials And Methods: From January 2005 to January 2020, 33 patients underwent surgical treatment of isolated locally recurrent RPS (LR group), locally recurrent RPS associated with the presence of distant recurrence (LR + DM group), and distant-only recurrent RPS (DM group).

A fast and accurate learning-based decoding algorithm for the classification of cardiovascular and respiratory challenges using intraneural electrodes in the pig vagus nerve.

Bioelectronic medicine is a new approach for developing closed-loop neuromodulation protocols on the peripheral nervous system (PNS) to treat a wide range of disorders currently treated with pharmacological approaches. Algorithms need to have low computational cost in order to acquire, process and model data for the modulation of the PNS in real time. Here, we present a fast learning-based decoding algorithm for the classification of cardiovascular and respiratory functional alterations (i.

A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs.

Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation of the ANS, the computational execution time and the memory and power demands of the decoding step are important factors to consider. In the context of cardiovascular and respiratory diseases, these requirements may partially explain why closed-loop clinical neuromodulation protocols that adapt stimulation parameters on patient's clinical characteristics are currently missing.