A Spiking Neural Network in sEMG Feature Extraction

Sensors

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

【 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.