  The muscle synergy concept provides the best framework to understand motor
control and it has been recently utilised in many applications such as
prosthesis control. The current muscle synergy model relies on decomposing
multi-channel surface Electromyography (EMG) signals into a synergy matrix
(spatial mode) and its weighting function (temporal mode). This is done using
several matrix factorisation techniques, with Non-negative matrix factorisation
(NMF) being the most prominent method. Here, we introduce a 4th-order tensor
muscle synergy model that extends the current state of the art by taking
spectral information and repetitions (movements) into account. This adds more
depth to the model and provides more synergistic information. In particular, we
illustrate a proof-of-concept study where the Tucker3 tensor decomposition
model was applied to a subset of wrist movements from the Ninapro database. The
results showed the potential of Tucker3 tensor factorisation in finding
patterns of muscle synergies with information about the movements and
highlights the differences between the current and proposed model.
