A Novel Feature Extraction for Robust EMG Pattern Recognition

TL;DR

This paper introduces two new features, MMNF and MMDF, for EMG pattern recognition that are robust against white Gaussian noise, eliminating the need for noise removal preprocessing.

Contribution

The paper proposes novel mean and median frequency features that tolerate WGN in EMG signals, improving recognition accuracy in noisy environments.

Findings

MMNF performs well in weak EMG signals with high noise levels.

Error rate of MMNF is about 5-10% at 0 dB SNR, outperforming other features.

Combining MMNF with Histogram and Willison amplitude enhances classification accuracy.

Abstract

Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering and solutions to remove WGN are limited. In addition, noise removal is an important step before performing feature extraction, which is used in EMG-based recognition. This research is aimed to present a novel feature that tolerate with WGN. As a result, noise removal algorithm is not needed. Two novel mean and median frequencies (MMNF and MMDF) are presented for robust feature extraction. Sixteen existing features and two novelties are evaluated in a noisy environment. WGN with various signal-to-noise ratios (SNRs), i.e. 20-0 dB, was added to the original EMG signal. The results showed that MMNF performed very well especially in weak EMG signal compared with others. The error of MMNF in weak EMG signal with very high noise, 0 dB SNR, is about 5-10 percent and closed by MMDF and Histogram, whereas the error of other features is more than 20 percent. While in strong EMG signal, the error of MMNF is better than those from other features. Moreover, the combination of MMNF, Histrogram of EMG and Willison amplitude is used as feature vector in classification task. The experimental result shows the better recognition result in noisy environment than other success feature candidates. From the above results demonstrate that MMNF can be used for new robust feature extraction.