  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.
