  Developing accurate hand gesture perception models is critical for various
robotic applications, enabling effective communication between humans and
machines and directly impacting neurorobotics and interactive robots. Recently,
surface electromyography (sEMG) has been explored for its rich informational
context and accessibility when combined with advanced machine learning
approaches and wearable systems. The literature presents numerous approaches to
boost performance while ensuring robustness for neurorobots using sEMG, often
resulting in models requiring high processing power, large datasets, and less
scalable solutions. This paper addresses this challenge by proposing the
decoding of muscle synchronization rather than individual muscle activation. We
study coherence-based functional muscle networks as the core of our perception
model, proposing that functional synchronization between muscles and the
graph-based network of muscle connectivity encode contextual information about
intended hand gestures. This can be decoded using shallow machine learning
approaches without the need for deep temporal networks. Our technique could
impact myoelectric control of neurorobots by reducing computational burdens and
enhancing efficiency. The approach is benchmarked on the Ninapro database,
which contains 12 EMG signals from 40 subjects performing 17 hand gestures. It
achieves an accuracy of 85.1%, demonstrating improved performance compared to
existing methods while requiring much less computational power. The results
support the hypothesis that a coherence-based functional muscle network encodes
critical information related to gesture execution, significantly enhancing hand
gesture perception with potential applications for neurorobotic systems and
interactive machines.
