The performance of upper-limb prostheses is currently limited by the relatively poor functionality of unintuitive control schemes. This paper proposes to extract, from multichannel electromyographic signals (EMG), motor neuron spike trains and project them into lower dimensional continuous signals, which are used as multichannel proportional inputs to control the prosthetic's actuators. These control signals are an estimation of the common synaptic input that the motor neurons receive. We use the simplest of metric learning approaches known as principal component analysis (PCA), as a linear unsupervised metric projection to extract the spectral information of high dimensional data into the subspace of the prosthetic hand degrees of freedom. We also investigate the importance of a rotation in the projection space that best aligns the PCA subspace with the space of degrees of freedom of the prosthetic hand, to attempt to approximate the sparseness properties of the motor axes (which are orthogonal), while no sparseness is enforced by PCA. Proof of concept for the feedforward path (open loop) is given by successful estimation of concurrent movements with up to three degrees of freedom. We also analyze and quantify the performance of the proposed decoding algorithm under implementation constraints and hardware limitations and propose a space-time subsampling strategy, to maximize projection fidelity, in each extracted source over time. The results confirm that the proposed decoding approach can directly project the motor neuron spike trains into kinematics for simultaneous and proportional prosthesis control that can be extended to multiple degrees of freedom. We show that the method is robust to reducing the training data in space (number of sources) and time, which makes it potentially suitable for clinical applications.