Paper Info
Title
Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities.
Abstract
In this study we developed a technique for identifying noisy electrodes in high density surface electromyography (HD-sEMG). The technique finds the spatial similarity of each electrode in the electrode array by counting the number of interactions the electrode has. Using this information the technique identifies noisy electrodes by finding electrodes that are significantly dissimilar to the other electrodes. The HD-sEMG recordings used in this study were taken from three participants who performed two isometric contractions of their biceps at 40% and 80% of their maximum voluntary contraction (MVC) force. White Gaussian noisy was added to a varying number of recorded signals before being digital filtering to generate a variety of recordings to test the technique with. In the recordings, groups of 2, 4, 8, and 16 electrodes had noise added such that the signal to noise ratios (SNR) were 0, 5, 10, 15, and 20dB. The results show that the technique can reliably identify groups of 2, 4, and 8 noisy electrodes with SNRs of 0, 5, and 10dB.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512846
EMBC
Field
DocType
Volume
Computer vision,Signal processing,Digital filter,Noise measurement,Electrode array,Pattern recognition,Computer science,Signal-to-noise ratio,Electromyography,Gaussian,Artificial intelligence,Electrode
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Adrian Bingham101.35
Beth Jelfs2629.40
Sridhar P. Arjunan3141.72
Dinesh K. Kumar4839.17