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
会议名称2015 International Conference on Advanced Mechatronic Systems (ICAMechS)
会议录名称Technical Poster Session
会议日期August 22-24, 2015
会议地点Beijing
摘要
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.
关键词Neucube Architecture Spiking Neural Network Emg Pattern Recognition Hand Motions
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12804
专题复杂系统管理与控制国家重点实验室_先进机器人
通讯作者Hou, Zengguang
作者单位1.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
推荐引用方式
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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