Identification of Contaminant Type in Surface Electromyography (EMG) Signals

P. McCool, G.D. Fraser, A.D.C. Chan, L. Petropoulakis, J.J. Soraghan

Research output: Contribution to journalArticle

24 Citations (Scopus)
Original languageEnglish
Pages (from-to)774-783
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number4
DOIs
Publication statusPublished - 21 Jan 2014

Keywords

  • AWGN
  • electromyography
  • medical signal processing
  • signal classification
  • support vector machines
  • EMG-enabled rehabilitation system
  • additive white Gaussian noise
  • amplifier saturation
  • classification system
  • contaminant type identification
  • electrocardiogram interference
  • motion artifact
  • power line interference
  • real EMG signals
  • signal quality
  • signal-to-noise ratio
  • simulated EMG signals
  • surface EMG signals
  • surface electromyography signals
  • Contamination
  • Electrocardiography
  • Electromyography
  • Interference
  • Muscles
  • Signal to noise ratio
  • Support vector machines
  • Biosignal quality analysis
  • classification
  • electromyography (EMG)
  • myoelectric signals
  • prostheses

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