Nonparametric, low bias, and low variance time-frequency analysis of myoelectric signals

Kristin A. Farry, Richard G. Baraniuk, Ian D. Walker

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

We apply Thomson's multiple window method to myoelectric signal time-frequency analysis for the first time. We extend Thomson's spectral estimation methodology for stationary signals to a time-frequency analysis tool, which we call the short-time Thomson transform. We compare this time-frequency analysis approach with the short-time Fourier transform.

Original languageEnglish (US)
Pages (from-to)993-994
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume17
Issue number2
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can
Duration: Sep 20 1995Sep 23 1995

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Nonparametric, low bias, and low variance time-frequency analysis of myoelectric signals'. Together they form a unique fingerprint.

Cite this