  Myoelectric control is an area of electromyography of increasing interest
nowadays, particularly in applications such as Hand Gesture Recognition (HGR)
for bionic prostheses. Today's focus is on pattern recognition using Machine
Learning and, more recently, Deep Learning methods. Despite achieving good
results on sparse sEMG signals, the latter models typically require large
datasets and training times. Furthermore, due to the nature of stochastic sEMG
signals, traditional models fail to generalize samples for atypical or noisy
values. In this paper, we propose the design of a Vision Transformer (ViT)
based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform
Hand Gesture Recognition from surface electromyography (sEMG) signals. The
proposed EMGTFNet architecture can accurately classify a variety of hand
gestures without any need for data augmentation techniques, transfer learning
or a significant increase in the number of parameters in the network. The
accuracy of the proposed model is tested using the publicly available NinaPro
database consisting of 49 different hand gestures. Experiments yield an average
test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793
trainable parameters. Our results outperform the ViT without FNB, thus
demonstrating that including FNB improves its performance. Our proposal
framework EMGTFNet reported the significant potential for its practical
application for prosthetic control.
