Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network.

@article{Cheron2007RecognitionOT,
  title={Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network.},
  author={Guy Cheron and Ana Maria Cebolla and Ana Bengoetxea and Françoise Leurs and Bernard Dan},
  journal={Neuroscience letters},
  year={2007},
  volume={414 2},
  pages={192-6}
}
Triphasic electromyographic (EMG) patterns with a sequence of activity in agonist (AG1), antagonist (ANT) and again in agonist (AG2) muscles are characteristic of ballistic movements. They have been studied in terms of rectangular pulse-width or pulse-height modulation. In order to take into account the complexity of the EMG signal within the bursts, we used a dynamic recurrent neural network (DRNN) for the identification of this pattern in subjects performing fast elbow flexion movements… CONTINUE READING
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Self-terminated fast movement of the forearm in man: amplitude dependence of the triple burst pattern

  • G. Cheron, E. Godaux
  • J. Biophys. Biom
  • 1986
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