HD-EMG Electrode Count and Feature Selection Influence on Pattern-based Movement Classification Accuracy

Abstract

Control schemes that rely on electromyography (EMG) pattern classification have shown to improve their accuracy when coupled with an increasing number of electrodes. In this study, HD-EMG signals from the hand and forearm of volunteers performing a series of movements were recorded. Different amounts of input EMG channels were selected and time-domain features were extracted to train several SVM classifiers. Detailed comparisons were made to evaluate the impact of electrode count and feature selection over the overall classification accuracy of 17 different movements. The increased resolution achieved from higher electrode counts yielded significant improvements in classification accuracy; however, these improvements were marginal when the number of channels utilized surpassed 100 electrodes.Clinical relevance- Pattern-based EMG classification is a widely used control method for a range of prosthetic devices and robotic interfaces. This work studies the optimal number of simultaneous HD-EMG channels and features that must be considered for accurate myoelectric control using this method.