EMG Features *

Time domain
Frequency domain
• Integrated EMG
• Autoregressive Coefficients
• SVD entropy
• Mean Absolute Value
• Modified Median Frequency
• Shannon entropy
• Modified Mean Absolute Value 1
• Modified Mean Frequency
• Fisher entropy
• Modified Mean Absolute Value 2
• Mean and Median Frequency
•Spectral entropy
• Mean Absolute Value Slope
• Simple Square Integral
• Variance of EMG
• Root Mean Square
• Waveform Length
• Zero Crossing
• Slope Sign Change
•Willison Amplitude
Time Domain Features
Integrated EMG
Integrated EMG (IEMG) is calculated as the summation of the absolute values of the sEMG signal amplitude. Generally, IEMG is used as an onset index to detect the muscle activity that used to oncoming the control command of assistive control device. It is related to the sEMG signal sequence firing point, which can be expressed as
where N denotes the length of the signal and represents the sEMG signal in a segment.
Mean Absolute Value
Mean Absolute Value (MAV) is similar to average rectified value (ARV). It can be calculated using the moving average of full-wave rectified EMG. In other words, it is calculated by taking the average of the absolute value of sEMG signal. It is an easy way for detection of muscle contraction levels and it is a popular feature used in myoelectric control application. It is defined as
Modified Mean Absolute Value 1
Modified Mean Absolute Value 1 (MMAV1) is an extension of MAV using weighting window function. It is shown as
Modified Mean Absolute Value 2
Modified Mean Absolute Value 2 (MMAV2) is similar to MMAV1. However, the smooth window is improved in this method using continuous weighting window function wn. It is given by
Mean Absolute Value Slope
Mean Absolute Value Slope (MAVSLP) is a modified version of MAV. The differences between the MAVs of adjacent segments are determined. The equation can be defined as
Simple Square Integral
Simple Square Integral (SSI) uses the energy of the sEMG signal as a feature. It can be expressed as
Variance of EMG
Variance of EMG (VAR) uses the power of the sEMG signal as a feature. Generally, the variance is the mean value of the square of the deviation of that variable. However, mean of EMG signal is close to zero. In consequence, variance of EMG can be calculated by
Root Mean Square
Root Mean Square (RMS) is modeled as amplitude modulated Gaussian random process whose RMS is related to the constant force and non-fatiguing contraction. It relates to standard deviation, which can be expressed as
The comparison between RMS and MAV feature is reported in the literatures [1, 2]. Clancy et al. experimentally found that the processing of MAV feature is equal to or better in theory and experiment than RMS processing. Furthermore, the measured index of power property that remained in RMS feature is more advantage than MAV feature.
Waveform Length
Waveform length (WL) is the cumulative length of the waveform over the time segment. WL is related to the waveform amplitude, frequency and time. It is given by
Zero Crossing
Zero crossing (ZC) is the number of times that the amplitude value of sEMG signal crosses the zero y-axis. In EMG feature, the threshold condition is used to abstain from the background noise. This feature provides an approximate estimation of frequency domain properties. It can be formulated as
Slope Sign Change
Slope Sign Change (SSC) is similar to ZC. It is another method to represent the frequency information of sEMG signal. The number of changes between positive and negative slope among three consecutive segments are performed with the threshold function for avoiding the interference in sEMG signal. The calculation is defined as
Willison Amplitude
Willison amplitude (WAMP) is the number of times that the  difference between sEMG signal amplitude among two adjacent segments that exceeds a  predefined threshold to reduce noise effects same as ZC and SSC. The definition is as
WAMP is related to the firing of motor unit action potentials (MUAP) and the muscle contraction level. The suitable value of threshold parameter of features in ZC, SSC, and WAMP is normally chosen between 10 and 100 mV that is dependent on the setting of gain value of instrument.
Frequency Domain Features
AR coefficient and AR modelling error
An autoregressive (AR) model can be used for prediction in a correlated time series. A variable in a correlated time series can be predicted from previous observations in the series by:
where are the parameters of the AR model and is a zero mean, white noise term accounting for the error in each prediction step. The parameters of the AR model are estimated over the first half of the epoch . The AR model is fit to the data over the first half of the epoch using the Yule-Walker method [12] and the model is used to perform one step ahead prediction on the second half of the epoch. The percentage error is then given by:
A total of 8 features are generated using this approach, corresponding to models of orders
Modified Median Frequency
Modified Median Frequency (MMDF) is the frequency at which the spectrum is divided into two regions with equal amplitude. It can be expressed as
where is the sEMG amplitude spectrum at frequency bin j.
Modified Mean Frequency
Modified Mean Frequency (MMNF) is the average frequency. MMNF is calculated as the sum of the product of the amplitude spectrum and the frequency, divided by the total sum of spectrum intensity, as in
where is the frequency of spectrum at frequency bin j.
Mean and Median Frequency
Traditional median frequency (MDF) and traditional mean frequency (MNF) are calculated based on power spectrum. We can calculate using the sEMG power spectrum instead of amplitude spectrum. They can be expressed as
The outline of amplitude spectrum and power spectrum is similar but the amplitude value of amplitude spectrum is larger than amplitude value of power spectrum. Moreover, the variation of amplitude spectrum is less than the power spectrum. For that reason, variation of MMNF and MMDF is also less than traditional MNF and MDF.
Shannon Entropy
Shannon entropy is a measure in information theory for estimating the uncertainty of an outcome [13]. It is the average unpredictability in a random variable, which is equivalent to its information content. To calculate Shannon entropy, the signal must first be represented as a discrete distribution. This is performed here by approximating the probability mass function by a 16-bin histogram. The Shannon entropy of the epoch is thus defined as:
where is the magnitude of each bin. If the entropy of is zero, the observer is certain of the future value of. Higher values of entropy then indicate increased uncertainty.
Spectral Entropy
Where the Shannon entropy is used to quantify the order in the EMG signal, spectral entropy is a measure of the order in the frequency spectrum of the EMG:
where i is a frequency index and is a normalised power spectral density :
SVD Entropy
Singular Value Decomposition (SVD) is a measure of the complexity of a signal, often used to obtain information about quasi-periodic signals in noise. The SVD algorithm decomposes a matrix such that:
where A is the input matrix, where U and V have orthogonal columns such that UTU = I and VTV = I, with I being the identity matrix and S is a diagonal vector of singular values. The singular values in S refer to the most significant underlying components in the signal. The number of singular values varies with the complexity of the signal, with an increase in signal complexity leading to a larger number of singular values. The number of significant singular values ζ1...ζdE can be obtained using Rissanen’s Minimum Description Length algorithm [14].
The SVD entropy calculates the entropy in the singular spectrum [14]. By performing SVD for an epoch as described in Equation above, the singular values ζ1...ζdE can be found. The SVD entropy is thus:
where dE is the singular dimension given by Rissanen’s Minimum description length, and where is the normalised singular values such that
SVD entropies should be lower for quasi-periodic signals such as EMG baseline oscillations due to movement.
Fisher Entropy
The Fisher information is calculated from the singular values of the EMG to describe the shape of the singular spectrum.
E. A. Clancy, E. L. Morin, and R. Merletti, “Sampling, Noisereduction and Amplitude Estimation Issues in Surface ElectroElectromyography,”Journal of Electromyography and Kinesiology, vol. 12,no. 1, pp. 1-16, Feb 2002, doi:10.1016/S1050-6411(01)00033-5..
E. A. Clancy and N. Hogan, “Theoretic and Experimental Comparison of Root-Mean-Square and Mean-Absolute-Value Electromyogram Amplitude Detectors,” Proc. nineteenth Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBS ’97), pp. 1267-1270, 1997.756605.
S.M. Kay. Modern spectral estimation. Pearson Education India, 1988.
C. E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27(3):379–423, 1949.
W. P. a. I. R. S.J. Roberts, Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing. Medical and Biological Engineering and Computing, 37(1):93–98, 1999.
* A. Phinyomark, C. Limsakul, P. Phukpattaranont. A novel feature extraction for robust EMG pattern recognition. J. Comput. 1 (1) (2009) 71-80
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