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

ana maria cebolla alvarez
Laboratory of Neurophysiology and Movement Biomechanics, Universit? Libre de Bruxelles

ana maria bengoetxea arrese
Laboratory of Neurophysiology and Movement Biomechanics, Universit? Libre de Bruxelles

fran?oise leurs
Laboratory of Neurophysiology and Movement Biomechanics, Universit? Libre de Bruxelles

bernard dan
Department of Neurology, Hopital Universitaire des Enfants reine Fabiola, Universit?e Libre de Bruxelles,

guy ch?ron
Laboratory of Neurophysiology and Movement Biomechanics, Universit? Libre de Bruxelles

     Full text: Not available
     Last modified: May 13, 2007

Abstract
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. Biceps and triceps EMGs were fed to all 35 fully-connected hidden units of the DRNN for mapping onto elbow angular acceleration signals. DRNN training was supervised, involving learning rule adaptations of synaptic weights and time constants of each unit.We demonstrated that the DRNN is able to perfectly reproduce the acceleration profile of the ballistic movements. Then we tested the physiological plausibility of all the networks that reached an error level below 0.001 by selectively increasing the amplitude of each burst of the triphasic pattern and evaluating the effects on the simulated accelerating profile. Nineteen percent of these simulations reproduced the physiological action classically attributed to the 3 EMG bursts: AG1 increase showed an increase of the first accelerating pulse, ANT an increase of the braking pulse and AG2 an increase of the clamping pulse. These networks also recognized the physiological function of the time interval between AG1 and ANT, reproducing the linear relationship between time interval and movement amplitude. This task-dynamics recognition has implications for the development of DRNN as diagnostic tools and prosthetic controllers.