Optimizing Feature Reduction and Selection Techniques for Surface Electromyography

EMG is a biological signal that monitors the electric impulses produced in muscles at various points during their contraction or relaxation, which are indicative of neuromuscular processes. The extraction of features from surface electromyography (sEMG) is a significant process to obtain useful information that is concealed in the data and to exclude redundant components and intrusions. Feature vector selection is crucial for effective EMG signal classification. However, several redundant characteristics have been found in studies of the categorization of EMG signals when utilizing a particular feature set. This research has discussed the properties and characteristics of different frequency and time domain features. Five features of the time domain are unnecessary and redundant and can be categorized based on the information and mathematical qualities, according to the scatter plots of different features, arithmetical analysis, or different classifiers. Contrarily, the statistical characteristics of the EMG spectral density of power are used to determine all frequency domain properties. This research also suggests that using inefficient features for classification in applications of EMG signals can be inappropriate due to class separability.