An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases
Expert Systems with Applications: An International Journal
An exploratory study to design a novel hand movement identification system
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Feature extraction of forearm EMG signals for prosthetics
Expert Systems with Applications: An International Journal
Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals
Expert Systems with Applications: An International Journal
EMG feature evaluation for improving myoelectric pattern recognition robustness
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.