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
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
108653Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None.