On the use of high-order cumulant and bispectrum formuscular-activity detection

Abstract

The electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.