{724} revision 2 modified: 04-07-2009 21:10 gmt

  • EMG pattern analysis and classification by Neural Network
    • 1989!
    • short, simple paper. showed that 20 patterns can accurately be decoded with a backprop-trained neural network.
  • PMID-18632358 Support vector machine-based classification scheme for myoelectric control applied to upper limb.
    • myoelectric discrimination with SVM running on features in both the time and frequency domain.
    • a survace MES (myoelectric sensor) is formed via the superposition of individual action potentials generated by irregular discharges of active motor units in a muscle fiber. It's amplitude, variance, energy, and frequency vary depending on contration level.
    • Time domain features:
      • Mean absolute value (MAV)
      • root mean square (RMS)
      • waveform length (WL)
      • variance
      • zero crossings (ZC)
      • slope sign changes (SSC)
      • William amplitude.
    • Frequency domain features:
      • power spectrum
      • autoregressive coefficients order 2 and 6
      • mean signal frequency
      • median signal frequency
      • good performance with just RMS + AR2 for 50 or 100ms segments. Used a SVM with a RBF kernel.
      • looks like you can just get away with time-domain metrics!!