Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments

International audienceThe probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR1 and KR2). The results show that at low SNR (<;5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR

EMG amplitude estimators based on probability distribution for muscle-computer interface

International audienceTo develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs

Optimal EMG amplitude detectors for muscle-computer interface

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Effects of Window Size and Contraction Types on the Stationarity of Biceps Brachii Muscle EMG Signals

International audienceIn order to analyze surface electromyography (EMG) signals, it is necessary to use techniques based on time (temporal) domain or frequency (spectral) domain. However, these techniques are based on the mathematical assumption of signal stationarity. On the other hand, EMG signal stationarity varies depending on analysis window size and contraction types. So in this paper, a suitable window size for an analysis of EMG during static and dynamic contractions was investigated using a stationarity test, the modified reverse arrangement test. More than 90% of the signals measured during static contraction can be considered as stationary signals for all window sizes. On average, a window size of 375 ms provides the most stationary information, 94.29% of EMG signals for static muscle contraction. For dynamic muscle contraction, the percentage of stationary signals decreased as the window size was increased. If the threshold of 80% stationarity was set to validate stationarity for each window size, a suitable window size should be 250 ms or lesser. For a real-time application that a size of analysis window plus processing time should be less than 300 ms, a window size of 250 ms is suggested for both contraction types

Effect of window size and contraction types on the stationarity of Biceps Brachii muscle EMG signals

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