Neural signal recording and processing in somatic neuroprosthetic applications. A review.

Neural decoding Neural interfaces Neuroprosthesis Peripheral nerve Signal processing

Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
01 05 2020
Historique:
received: 09 05 2019
revised: 30 11 2019
accepted: 26 02 2020
pubmed: 3 3 2020
medline: 22 6 2021
entrez: 2 3 2020
Statut: ppublish

Résumé

Neurointerfaces have acquired major relevance as both rehabilitative and therapeutic tools for patients with spinal cord injury, limb amputations and other neural disorders. Bidirectional neural interfaces are a key component for the functional control of neuroprosthetic devices. The two main neuroprosthetic applications of interfaces with the peripheral nervous system (PNS) are: the refined control of artificial prostheses with sensory neural feedback, and functional electrical stimulation (FES) systems attempting to generate motor or visceral responses in paralyzed organs. The results obtained in experimental and clinical studies with both, extraneural and intraneural electrodes are very promising in terms of the achieved functionality for the neural stimulation mode. However, the results of neural recordings with peripheral nerve interfaces are more limited. In this paper we review the different existing approaches for PNS signals recording, denoising, processing and classification, enabling their use for bidirectional interfaces. PNS recordings can provide three types of signals: i) population activity signals recorded by using extraneural electrodes placed on the outer surface of the nerve, which carry information about cumulative nerve activity; ii) spike activity signals recorded with intraneural electrodes placed inside the nerve, which carry information about the electrical activity of a set of individual nerve fibers; and iii) hybrid signals, which contain both spiking and cumulative signals. Finally, we also point out some of the main limitations, which are hampering clinical translation of neural decoding, and indicate possible solutions for improvement.

Identifiants

pubmed: 32114143
pii: S0165-0270(20)30075-3
doi: 10.1016/j.jneumeth.2020.108653
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

108653

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Stanisa Raspopovic (S)

Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zürich, Switzerland.

Andrea Cimolato (A)

Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zürich, Switzerland; NEARLab - Neuroengineering and Medical Robotics Laboratory, DEIB Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milano, Italy; IIT Central Research Labs Genova, Istituto Italiano Tecnologia, 16163, Genova, Italy.

Alessandro Panarese (A)

The BioRobotics Institute, Scuola Superiore Sant'Anna, I-56127, Pisa, Italy.

Fabio Vallone (F)

The BioRobotics Institute, Scuola Superiore Sant'Anna, I-56127, Pisa, Italy.

Jaume Del Valle (J)

Institute of Neurosciences and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma De Barcelona, CIBERNED, 08193, Bellaterra, Spain.

Silvestro Micera (S)

The BioRobotics Institute, Scuola Superiore Sant'Anna, I-56127, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale De Lausanne, Lausanne, CH-1015, Switzerland. Electronic address: silvestro.micera@epfl.ch.

Xavier Navarro (X)

Institute of Neurosciences and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma De Barcelona, CIBERNED, 08193, Bellaterra, Spain; Institut Guttmann De Neurorehabilitació, Badalona, Spain. Electronic address: xavier.navarro@uab.cat.

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