A Spiking Neural Network in sEMG Feature Extraction
AbstractWe 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. View Full-Text
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Lobov, S.; Mironov, V.; Kastalskiy, I.; Kazantsev, V. A Spiking Neural Network in sEMG Feature Extraction. Sensors 2015, 15, 27894-27904.
Lobov S, Mironov V, Kastalskiy I, Kazantsev V. A Spiking Neural Network in sEMG Feature Extraction. Sensors. 2015; 15(11):27894-27904.Chicago/Turabian Style
Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor. 2015. "A Spiking Neural Network in sEMG Feature Extraction." Sensors 15, no. 11: 27894-27904.