The Spiking Rates Inspired Encoder and Decoder for Spiking Neural Networks: An Illustration of Hand Gesture Recognition

The spiking neural network (SNN) is the third generation of artificial neural networks. The transmission and expression of information in SNN are performed by spike trains, making the SNN have the advantages of high calculation speed and low power consumption. Recently, researchers have employed the SNN to recognize surface electromyography (sEMG) signals, but problems are still left. The sEMG encoders may cause information loss, and the network decoders may cause poor training performance. The strength of the neuron stimulated can be expressed by the frequency of the input or output spikes (namely firing rate). Inspired by the firing rate principle, we proposed the smoothed frequency-domain decomposition encoder, which converts the sEMG to spike trains. Furthermore, we also proposed the network efferent energy decoder, which converts the network output to recognizing results. The employed SNN is a three-layer fully-connected network trained by the grey wolf optimizer. The proposed methods are verified by a hand gestures recognition task. A total of 11 subjects participated in the experiment, and sEMG signals were acquired from five commonly used hand gestures by three sEMG sensors. The results indicate that the loss function can be reduced to below 0.4, and the average gesture recognizing accuracy is 91.21%. These results show the potential of using the proposed methods for the actual prosthesis. In the future, we will optimize the SNN training method to improve the training speed and stability.