Paper Info
Title
Second-order information bottleneck based spiking neural networks for sEMG recognition
Abstract
The pattern recognition of surface electromyography (sEMG) signal is an important application in the realization of human-machine interface. However, due to the disturbance of human body, sensors and environment, sEMG signal usually contains lots of noise, which brings great challenges to the high-accuracy sEMG pattern recognition. In addition, embedded human wearable devices are becoming more and more popular nowadays. How to realize the sEMG recognition method with low power consumption and high noise immunity has also become a difficult and very meaningful research topic. In this paper, a spiking neural network (SNN) classification method based on second-order information bottleneck training is proposed. Firstly, the training loss function for classification neural networks is constructed based on the proposed second-order information bottleneck. The method is used to train the conventional continuous-valued neural network and convert it into an SNN model with equivalent structure and connection weights. Then, the converted SNN is used to classify the sEMG signal patterns. Through a series of theoretical analysis and experimental results, it is proved that this method has significant advantages in terms of generalization of network determination and computational efficiency. The experimental code can be accessed from https://github.com/anvien/2OIB-for-sEMG-Recognition.
Year
DOI
Venue
2022
10.1016/j.ins.2021.11.065
Information Sciences
Keywords
DocType
Volume
Surface electromyography (sEMG) recognition,Spiking neural network,Second-order information bottleneck
Journal
585
ISSN
Citations 
PageRank 
0020-0255
2
0.39
References 
Authors
0
5
Name
Order
Citations
PageRank
Anguo Zhang154.17
Yuzhen Niu220.39
Yueming Gao320.39
Junyi Wu421.74
Zhipeng Gao510031.83