Low-density surface electromyographic patterns under electrode shift: Characterization and NMF-based classification

Abstract

Electrode shift causes high variability and non-stationarity in surface electromyographic (sEMG) patterns, which seriously impairs the robustness of sEMG-based prosthetic control in daily use. Existing methods for electrode shift are mostly suitable for high-density sEMG configuration and do not work well for low-density sEMG. Nowadays, a quantitative characterization of the influence of electrode shift on low-density sEMG and an effective classification method to handle the influence of electrode shift are still lacking. The present study first designed an experiment to produce electrode shift in an 8-channel sEMG recording system. By using tSNE and other quantitative indices, we observed that rotating electrodes’ position led to great changes in the TD feature space, which subsequently decreased the classification accuracy. Further, we showed that existing algorithms against electrode shift in a high-density electrode configuration have limited effect for low-density sEMG recordings. To combat the influence of electrode shift and to improve the classification accuracy, the nonnegative matrix factorization (NMF) algorithm was used to reduce the non-stationarity of sEMG features and the self-enhancing linear discriminant analysis (SE-LDA) method was adopted to update the classifier based on the changes of sEMG features space. Results showed that the NMF with SE-LDA method achieved an accuracy of 70.58 ± 18.08% for the 10-motion classification problem with electrode shift, which was much higher than the accuracy of 54.84 ± 12.24% achieved by the classical TD features with the LDA classifier. The robust and effective new method against the electrode-shift problem has a great potential for the design of practical sEMG-based prosthetic control.

Gan Huang

My research interests include Neural Modulation, Brain Computer Interface and Neural Prosthetics.