Peripheral brain interfacing: Reading high-frequency brain signals from the output of the nervous system

TL;DR

This paper explores non-invasive peripheral neural interfaces that utilize muscle sensors and deep learning to decode CNS activity by analyzing spinal motor neuron signals, offering a promising alternative to invasive methods.

Contribution

It introduces the concept of using muscle sensors and deep learning to estimate CNS signals from motor neuron outputs, advancing non-invasive neural interfacing techniques.

Findings

Decoding spinal motor neuron activity with high accuracy.

Muscle sensors can estimate CNS signals reaching MNs.

Potential for non-invasive neural interfaces.

Abstract

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking activity of spinal motor neurons (MNs) in real time and with high accuracy. MNs represent the motor output layer of the CNS, receiving and sampling signals originating in different regions in the nervous system, and generating the neural commands that control muscles. The input signals to MNs can be estimated from the MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some neural activity from the CNS that reaches the MNs but does not directly modulate force production. We also discuss the evidence supporting this concept, and the necessary advances to consolidate and test MN-based CNS interfaces in controlled and real-world settings.