Despite the promising features of neural interfaces, their trade-off between information transfer and invasiveness has limited translation and viability outside research settings. Here, we present a non-invasive neural interface that provides access to spinal motoneuron activities from a sensor band at the wrist. The interface decodes electric signals present at the tendon endings of the forearm muscles by using a model of signal generation and deconvolution. First, we evaluated the reliability of the interface to detect motoneuron firings, and thereafter we used the decoded neural activity for the prediction of finger movements in offline and real-time conditions. The results showed that motoneuron activity decoded from the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time control. These findings demonstrate the feasibility of a wearable, non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.