Study of EMG Feature Selection for Hand Motions Classification
Abstract
In recent days, electromyography(EMG) pattern recognition has becoming one ofthe major interests in rehabilitation area. However, EMG feature set normally consists of relevant, redundant and irrelevant features. To achievehigh classification performance, the selection ofpotential features is critically important. Thus, this paper employs two recent feature selection methods namely competitive binary gray wolfoptimizer (CBGWO) and modified binary treegrowth algorithm (MBTGA) to evaluate the mostinformative EMG feature subset for efficient classification. The experimental results show thatCBGWO and MBTGA are not only improves theclassification performance, but also reduces thenumber of features.
Keywords— Electromyography; feature extraction; time domain feature; featureselection; classification
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