 The study proposes a new approach to estimate the neural drive using a deep convolutional neural network, CNN. It uses data from different contraction tasks, different intensities, learns general features of MUAP variants, and estimates the neural drive for other contraction tasks. This allows for a more efficient and accurate neural drive based human machine interface that is generalizable to different contraction tasks without retraining. This article was authored by Yuan, Sanjun J. Kim, Simon Avrilon, and others.