Electromyography is a valuable tool in many clinical analyses, as it can give the clinician an accurate representation of what the muscles are doing to contribute to the desired task. For functional EMG the surface electrodes have the advantage of convenience and comfort. The major disadvantage to surface electrodes are cross talk and low level signal reception. Their adverse effects complicate the definition of muscle timing and relative intensity of the activity. During period of low muscle activity there is the possibility that the EMG signals may include signals from musculature other than the muscle of interest. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. In order to define suitable search criteria the approximation of nagentropy is used as a contrast function for minimizing the statistical dependence between the component. The fast ICA algorithm given by Aapo Hyvarinen identify the independent source by maximizing the joint entropy of a set of output signals derived form the Rectus femori (RF) and semimembranous (S) muscles of lower limb during pedaling action of leg. The signal of RF severely affected by sartorius and Tensor facia lata and the S muscle includes signals from Sartorius and Semitendenus together with electrical noise is separated via fast ICA algorithm. Result illustrating the good performance of the method.
Cite as: Somkuwar, A., Guha, S.K., Atreya, S. (2001) Independent component analysis of electromyographic signal. Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001), 217-221
@inproceedings{somkuwar01_maveba,
author={Ajay Somkuwar and Sujoy K. Guha and Sudhir Atreya},
title={{Independent component analysis of electromyographic signal}},
year=2001,
booktitle={Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001)},
pages={217--221}
}