Ibáñez Jaime, Zicher Blanka, Burdet Etienne, Baker Stuart N, Mehring Carsten, Farina Dario
BSICoS Group, Departamento de Ingeniería Electrónica y Comunicaciones, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain.
BSICoS Group, Instituto de Investigación Sanitaria (IIS) Aragón, Zaragoza, Spain.
Nat Biomed Eng. 2025 Jun 27. doi: 10.1038/s41551-025-01445-1.
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.
准确且可靠地记录和解读中枢神经系统（CNS）对于人机交互技术的进步至关重要。直接测量中枢神经系统活动的技术受到其分辨率、对干扰的敏感性以及侵入性的限制。运动神经元（MNs）代表中枢神经系统的运动输出层，接收并采样来自神经系统不同区域的信号，并生成控制肌肉的神经指令。肌肉记录和深度学习能够实时且高精度地解码脊髓运动神经元的放电活动。运动神经元的输入信号可从其输出中估算得出。在此，我们认为使用肌肉传感器的外周神经接口是一种有前景的非侵入性方法，可用于估算来自中枢神经系统且到达运动神经元但不直接调节力产生的部分神经活动。我们讨论了支持这一概念的证据，以及在受控和现实环境中巩固和测试基于运动神经元的中枢神经系统接口所需的进展。
