## 6 Features Extraction  
In the feature extraction process, the size of the raw data was reduced to be able to input these parameters to the Machine Learning (ML) classification model. In general, sEMG data contain important and irrelevant information. The irrelevant information should be discarded to reduce the dimensionality of the features vector by mapping sEMG data to another space. This step is important  
Figure 3: Acquisition of sEMG data of a user to create the database (Enrolment).  
for extracting the main features from the data of each user that aid in distinguishing between the enrolled users [36; 37].  
The calculation of the Power Spectral Density (PSD) of the sEMG signal is important, since it is calculated by using the relevant parameters that are used for the authentication of users. The PSD depicts the density of a signal with reference to the frequency. The main purpose of spectral density calculation is to capture the spectral density of sEMG signal from a series of time samples. There are two different techniques used in the estimation of PSD, parametric, and non-parametric. The estimated PSD is calculated directly from the signal in the Nonparametric techniques. The most known simple method is called periodogram. In periodogram method, discrete-time Fourier transform of sampled signal is calculated first, and then the magnitude squared of the result is calculated [38]. In this research, the PSD is estimated by periodogram applying [PERSON]'s method, as in [39]. The power of the sEMG signal is estimated against frequency in order to reduce the noise. The signal is converted from time domain to frequency domain using periodograms [40].  
After estimating the PSD of sEMG signals of the constructed database, different frequncy domain features are extracted for the purpose of feeding the classifier with the feature vector for the verification of individuals in the proposed biometrics verification system. These parameters are Kurtosis, signal power, deciles, median frequency, frequency peak and dissymmetry coefficient and frequency peak.  
The selected features characterize the sEMG signal to be able to differentiate between the users data stored in the database. The power of a signal represents the distribution of energy along the signal. The average frequency is used to calculate the average of the signal in frequency domain. The kurtosis measure of the combined weight of a distribution's tails relative to the center of the distribution. The median frequency is calculated in order to divide the PSD into two sections, 50% of data are less than the median and 50% are greater This can be generalized, so that the segments of the distribution can be four, ten, or \(n\) segments. These calculated parameters are called quartiles, deciles, percentiles, or quantiles. The coefficient of dissymmetry offers information pertaining to the profile of the PSD from the respective of symmetry. The peak frequency is the frequency at the maximum of the PSD.  
These features are called frequency domain features of the sEMG signal. Time domain features were also calculated for better accuracy in classification. These include length or duration of data and the root mean square (RMS) of each segment of the time domain signal. In this study, the EMG signal was segmented into 10 equal lengths. as shown in Figure 4.  
The features extracted from sEMG signals are summarized and listed in Table 1.  
Figure 4: Segmentation of Electromyography (EMG) signal.