Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements

Abstract Different approaches have been proposed to select features and channels for pattern recognition classification of myoelectric upper-limb prostheses. The goal of this work is to use deterministic methods to select the feature-channels pairs that best classify the hand postures at different limb positions. Two selection methods were tried. One is a distance-based feature selection (DFSS) that determines a separability index using the Mahalanobis distance between classes. The second method is a correlation-based feature selection (CFSS) that measures the amount of mutual information between features and classes. To evaluate the performance of these selection methods, EMG data from 10 able-bodied subjects were acquired when performing 5 hand postures at 9 different arm positions and 10 time-domain and frequency-domain features were extracted. Classification accuracy using both methods was always higher than including all the features and channels and showed slight improvement over classification using the state-of-art TD features when evaluated against limb variation. The CFSS method always used less feature-channel pairs compared to the DFSS method. Using both methods, selection of channels placed on the posterior side of the forearm was significantly higher than anterior side. Such methods could be used as fast screening filters to select features and channels that best classify different hand postures at different arm positions.