Design of Adaptive Filter Using JordanElman Neural Network in a Typical EMG Signal Noise Removal

Abstract EN

The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG).

These EMG signals are low-frequency and lower-magnitude signals.

In this paper, it is presented that Jordan/Elman neural network can be effectively used for EMG signal noise removal, which is a typical nonlinear multivariable regression problem, as compared with other types of neural networks.

Different neural network (NN) models with varying parameters were considered for the design of adaptive neural-network-based filter which is a typical SISO system.

The performance parameters, that is, MSE, correlation coefficient, N/P, and t, are found to be in the expected range of values.