  Accurate hand gesture prediction is crucial for effective upper-limb
prosthetic limbs control. As the high flexibility and multiple degrees of
freedom exhibited by human hands, there has been a growing interest in
integrating deep networks with high-density surface electromyography (HD-sEMG)
grids to enhance gesture recognition capabilities. However, many existing
methods fall short in fully exploit the specific spatial topology and temporal
dependencies present in HD-sEMG data. Additionally, these studies are often
limited number of gestures and lack generality. Hence, this study introduces a
novel gesture recognition method, named STGCN-GR, which leverages
spatio-temporal graph convolution networks for HD-sEMG-based human-machine
interfaces. Firstly, we construct muscle networks based on functional
connectivity between channels, creating a graph representation of HD-sEMG
recordings. Subsequently, a temporal convolution module is applied to capture
the temporal dependences in the HD-sEMG series and a spatial graph convolution
module is employed to effectively learn the intrinsic spatial topology
information among distinct HD-sEMG channels. We evaluate our proposed model on
a public HD-sEMG dataset comprising a substantial number of gestures (i.e.,
65). Our results demonstrate the remarkable capability of the STGCN-GR method,
achieving an impressive accuracy of 91.07% in predicting gestures, which
surpasses state-of-the-art deep learning methods applied to the same dataset.
