Comparison of pre-processing filters on the performance of sEMG based pattern recognition

The noise present in the surface electromyography (sEMG) signals is a significant problem in the control of the rehabilitation scheme. Different noise reducing methods have been discussed and considered individually in previous studies. However, there is limited work on the comparison of different noise reduction strategies. To achieve good performance of Myoelectric Control (MEC) system, the selection of filters becomes essential. The vital contribution of this work is a study into the comparison of three denoising methods including Butterworth filter, Weiner filter and Spectral Subtraction (SS) filter that has been used to remove the noise from sEMG signal. Performance evaluation of the three noise reduction methods is done regarding classification accuracy and computation time. The three denoising methods have been validated on the recorded sEMG of seven healthy subjects while performing eight classes of movements from the two muscle positions on the right forearm. The accuracy is compared with four classifiers namely, J48, k-nearest neighbors (KNN), Naive Bayes and Linear Discriminant Analysis (LDA). Results show that the Butterworth filter provides marginally better performance than the other two filters regarding classification accuracy; when computation time is considered SS filter offers significant savings. A visual inspection of the output of the Weiner filter hints at its utility as a muscle activity onset detection tool.