  The use of deep neural networks in electromyogram (EMG) based prostheses
control provides a promising alternative to the hand-crafted features by
automatically learning muscle activation patterns from the EMG signals.
Meanwhile, the use of raw EMG signals as input to convolution neural networks
(CNN) offers a simple, fast, and ideal scheme for effective control of
prostheses. Therefore, this study investigates the relationship between window
length and overlap, which may influence the generation of robust raw EMG
2-dimensional (2D) signals for application in CNN. And a rule of thumb for a
proper combination of these parameters that could guarantee optimal network
performance was derived. Moreover, we investigate the relationship between the
CNN receptive window size and the raw EMG signal size. Experimental results
show that the performance of the CNN increases with the increase in overlap
within the generated signals, with the highest improvement of 9.49% accuracy
and 23.33% F1-score realized when the overlap is 75% of the window length.
Similarly, the network performance increases with the increase in receptive
window (kernel) size. Findings from this study suggest that a combination of
75% overlap in 2D EMG signals and wider network kernels may provide ideal motor
intents classification for adequate EMG-CNN based prostheses control scheme.
