Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities

Bingham, A, Jelfs, B, Poosapadi Arjunan, S and Kumar, D 2018, 'Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities', in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, USA, 18-21 July 2018, pp. 2325-2328.


Document type: Conference Paper
Collection: Conference Papers

Title Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities
Author(s) Bingham, A
Jelfs, B
Poosapadi Arjunan, S
Kumar, D
Year 2018
Conference name 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Conference location Honolulu, USA
Conference dates 18-21 July 2018
Proceedings title 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher IEEE
Place of publication USA
Start page 2325
End page 2328
Total pages 4
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
Subjects Biomedical Instrumentation
Signal Processing
Copyright notice © 2018 IEEE
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