EMG feature extraction for tolerance of white Gaussian noise

White Gaussian Noise (WGN) is a major problem in analysis of surface electromyography (sEMG) signals. Therefore, noise reduction is an important step before performing feature extraction, which is used in EMG-based gestures classification. However, the solutions to remove white Gaussian noise are limited. This research is aimed to select a feature that tolerate with white Gaussian noise. As a result, noise removal algorithms are not needed. Eight features in time domain and frequency domain were tested with additive white Gaussian noise at various signal-to-noise ratios (SNRs). Results showed that Willison amplitude was the best feature comparing with others. Subsequently, evaluations of threshold of Willison amplitude were tested between 5-50 mV. Willison amplitude with 5 mV threshold showed better than others. From the above experiment results, it is shown that Willison amplitude with 5 mV thresholds can use for feature extraction and can exclude removal noise algorithm.