Myoelectric signal classification using a finite impulse response neural network

Recent work by Hudgins (1993) has proposed a neural network-based approach to classifying the myoelectric signal (MES) elicited at the onset of movement of the upper limb. A standard feedforward artificial network was trained (using the backpropagation algorithm) to discriminate amongst four classes of upper-limb movements from the MES, acquired from the biceps and triceps muscles. The approach has demonstrated a powerful means of classifying limb function intent from the MES during natural muscular contraction, but the static nature of the network architecture fails to fully characterize the dynamic structure inherent in the MES. It has been demonstrated previously that a finite-impulse response (FIR) network has the ability to incorporate the temporal structure of a signal, representing the relationships between events in time and providing translation invariance of the relevant feature set. The application of this network architecture to limb function discrimination from the MES is described here.