Robust outlier detection in high density surface electromyographic signals

Abstract

High Density surface Electromyography (HDsEMG) has been applied in both research and clinical applications for non-invasive neuromuscular assessment in several different fields using 2-D array. Proper interpretation of HDsEMG signals requires identifying “good” channels (where there is no short-circuit or bad-contact or major power line interference problem). Recording with many channels usually implies bad-contacts (that introduces large power line interference) and short-circuits (when using gels). In addition to online monitoring the electrode-contact quality, it is necessary to identify “bad” channels, or outliers, prior to the analysis of HDsEMG signal. In this paper we introduce a robust method to identify outliers in a set of monopolar HDsEMG signals recorded from Biceps and Triceps Brachii,Anconeus, Brachioradialis and Pronator Teres. The sensitivity and precision of this method show that this approach is promising.