Editorial: Closed-Loop Systems for Next-Generation Neuroprostheses

Abstract : Millions of people worldwide are affected by neurological disorders which disrupt the connections within the brain and between brain and body causing impairments of primary functions and paralysis. Such a number is likely to increase in the next years and current assistive technology is yet limited. A possible response to such disabilities, offered by the neuroscience community, is given by Brain-Machine Interfaces (BMIs) and neuroprosthetic research. The latter field of research is highly multidisciplinary, since it involves very different and disperse scientific communities, making it fundamental to create connections and to join research efforts. Indeed, the design and development of neuroprostheses involve different research topics such as: interfacing to nervous systems at different levels of architectural complexity (from in vitro neuronal ensembles to human brain), bio-electronic interfaces for stimulation (e.g., micro-stimulation, DBS: Deep Brain Stimulation) and recording (e.g., EMG, Electromyography; EEG, Electroencephalography; LFP, Local Field Potential), innovative signal processing tools for coding and decoding of neural activity, biomimetic artificial Spiking Neural Networks (SNN) and neural network modeling (Indiveri et al., 2001; Bonifazi et al., 2013). In order to develop functional communication with the nervous system and to create a new generation of neuroprostheses, the study of closed-loop systems is mandatory. It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes than open-loop devices. Improvements in task performance, usability, and embodiment have all been reported in systems utilizing some form of feedback. The bi-directional communication between living neurons and artificial devices is the main final goal of those studies. However, closed-loop systems not only based on visual feedback are still uncommon, mostly due to requirement of multidisciplinary effort. Only few examples in this direction can be cited from the literature, such as O'Doherty et al. (2011) and Capogrosso et al. (2016). Therefore, through this research topic on closed-loop systems for next-generation neuroprostheses, we encourage an active discussion among neurobiologists, electrophysiologists, bioengineers, computational neuroscientists, and neuromorphic engineers. This Editorial aims to facilitate this process by ordering the 25 contributions of this research in which we highlighted in three different parts: (A) Optimization of different blocks composing the closed-loop system, (B) Systems for neuromodulation based on DBS, EMG, and SNN, and (C) Closed-loop BMIs for rehabilitation. (A) OPTIMIZING THE DIFFERENT BLOCKS COMPOSING A CLOSED-LOOP SYSTEM To design closed-loop neuroprostheses, the three main blocks which require optimization are recording, signal processing, and stimulation.
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Timothée Levi, Paolo Bonifazi, Paolo Massobrio, Michela Chiappalone. Editorial: Closed-Loop Systems for Next-Generation Neuroprostheses. Frontiers in Neuroscience, Frontiers, 2018, 12, pp.26 - 26. ⟨10.3389/fnins.2018.00026⟩. ⟨hal-01709420⟩

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