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

Adrian Bingham, Beth Jelfs, Sridhar P. Arjunan, Dinesh K. Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherIEEE
Pages2325-2328
Number of pages4
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - 26 Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint

Dive into the research topics of 'Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities'. Together they form a unique fingerprint.

Cite this