期刊论文详细信息
Sensors
A Spiking Neural Network in sEMG Feature Extraction
Sergey Lobov1  Vasiliy Mironov2  Innokentiy Kastalskiy2  Victor Kazantsev2  Steffen Leonhardt2 
[1] Department of Neurotechnology, Lobachevsky State University of Nizhni Novgorod, 23 Gagarin Ave., Nizhny Novgorod 603950, Russia;
关键词: sEMG;    feature extraction;    pattern classification;    artificial neural network;    neurointerface;    exoskeleton;   
DOI  :  10.3390/s151127894
来源: mdpi
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【 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.

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