CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Feasibility of NeuCube Spiking Neural Network Architecture for EMG Pattern Recognition
Peng, Long1; Hou, Zengguang1; Kasabov, Nikola2; Bian, Guibin1; Vladareanu, Luige3; Yu, Hongnian4
2015-08
Conference Name2015 International Conference on Advanced Mechatronic Systems (ICAMechS)
Source PublicationTechnical Poster Session
Conference DateAugust 22-24, 2015
Conference PlaceBeijing
Abstract
Multichannel electromyography (EMG) signals have been used as human-machine interface (HMI) for the control of pattern-recognition based prosthetic system in recent years. This paper is a feasibility analysis of using recently proposed NeuCube spiking neural network (SNN) architecture for a 6-class recognition problem of hand motions. NeuCube is an integrated environment, which uses SNN reservoir and dynamic evolving SNN classifier. NeuCbube has the advantage of processing complex spatio-temporal data. The preliminary experiments show that Neucube is more efficient for EMG classification than commonly used machine learning techniques since it achieves better accuracy as well as consistent classification outcomes. The performance of NeuCube combined with TD features reaches up to 95.33% accuracy after a careful selection of the features. This paper demonstrates that NeuCube has the potential to be employed in practical applications of myoelectric control.
KeywordNeucube Architecture Spiking Neural Network Emg Pattern Recognition Hand Motions
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12804
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorHou, Zengguang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Knowledge Engineering and Discovery Research Institute, Auckland University of Technology
3.Institute of Solid Mechanics, Romanian Academy
4.School of Design, Engineering & Computing, Bournemouth University
Recommended Citation
GB/T 7714
Peng, Long,Hou, Zengguang,Kasabov, Nikola,et al. Feasibility of NeuCube Spiking Neural Network Architecture for EMG Pattern Recognition[C],2015.
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