Peripheral neural interfaces for reading high-frequency brain signals
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human–machine interfacing. Technologies for direct measurements of CNS activity are limited by their resolution, sensitivity to interference and invasiveness. Motor neurons (MNs) represent the motor output layer of the CNS, receiving and sampling signals from different regions in the nervous system and generating the neural commands that control muscles. Muscle recordings and deep learning decode the spiking activity of spinal MNs in real time and with high accuracy. The input signals to MNs can be estimated from MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some of the neural activity from the CNS that reaches the MNs but does not directly modulate force production. We discuss the evidence supporting this concept and the advances needed to consolidate and test MN-based CNS interfaces in controlled and real-world settings.
Date Issued
2025-09-01
Date Acceptance
2025-05-29
Citation
Nature Biomedical Engineering, 2025, 9 (9), pp.1391-1402
ISSN
2157-846X
Publisher
Nature Research
Start Page
1391
End Page
1402
Journal / Book Title
Nature Biomedical Engineering
Volume
9
Issue
9
Copyright Statement
Copyright © Springer Nature Limited 2025. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40579488
PII: 10.1038/s41551-025-01445-1
Subjects
COMMON SYNAPTIC INPUT
CORTICAL ACTIVITY
CORTICOMUSCULAR COHERENCE
DRIVE
EMG
Engineering
Engineering, Biomedical
MONKEY MOTOR CORTEX
MUSCLE
POPULATION
Science & Technology
SYNCHRONIZATION
Technology
UNITS
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2025-06-27