A Spiking Neural Network in sEMG Feature Extraction

被引：29

作者：

Lobov, Sergey ^{[1
]}

Mironov, Vasiliy ^{[1
]}

Kastalskiy, Innokentiy ^{[1
]}

Kazantsev, Victor ^{[1
]}

机构：

[1] Lobachevsky State Univ Nizhni Novgorod, Dept Neurotechnol, Nizhnii Novgorod 603950, Russia

来源：

SENSORS | 2015年 / 15卷 / 11期

关键词：

sEMG; feature extraction; pattern classification; artificial neural network; neurointerface; exoskeleton; MODEL;

D O I：

10.3390/s151127894

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

引用

页码：27894 / 27904

页数：11

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