Peripheral neural interfaces for reading high-frequency brain signals

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.