Using principal-components regression to stabilize EMG-muscle force parameter estimates of torso muscles

Models for estimating muscle force from surface electromyographic (EMG) recordings require parameter estimates with low intertrial variability. The inclusion of multiple muscles in multivariate statistical models can lead to multicollinearity, especially when there are significant correlations between synergist muscles. One result of multicollinearity is that parameter estimates are very sensitive to changes in the independent variables. This study compared the parameter variability of multiple regression and principal-components regression techniques when applied to a six muscle EMG analysis of the lumbar region of the torso. Nine subjects participated. Twenty-three percent of the traditional multiple-regression parameters had incorrect signs, but none of the principal-components regression parameters did. The principal-components regression technique also produced parameter estimates having an order of magnitude smaller parameter variability. It was concluded that principal-components regression is an effective method of mitigating the effect of multicollinearity in torso EMG models.