**1. Introduction**

Computational Intelligence in Electromyography Analysis – 194 A Perspective on Current Applications and Future Challenges

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Rich useful information can be obtained from the muscles and researchers can use such information in a wide class of clinical and engineering applications by measuring surface electromyography (EMG) signals (Merletti & Parker, 2004). Normally, EMG signals are acquired by surface electrodes that are placed on the skin superimposed on the targeted muscle. In order to use the EMG signal as a diagnosis signal or a control signal, a feature is often extracted before performing analysis or classification stage (Phinyomark et al., 2012a) because a lot of information, both useful information and noise (Phinyomark et al., 2012b), is contained in the raw EMG data. An EMG feature is a distinct characteristic of the signal that can be described or observed quantitatively, such as being large or small, spiky or smooth, and fast or slow. Generally, EMG features can be computed in numerical form from a finite length time interval and can change as a function of time, i.e. a voltage or a frequency. They can be computed in several domains, such as time domain, frequency domain, timefrequency and time-scale representations (Boostani & Moradi, 2003). However, frequencydomain features show the better performance than other-domain features in case of the assessing muscle fatigue (Al-Mulla et al., 2012). Mean frequency (MNF) and median frequency (MDF) are the most useful and popular frequency-domain features (Phinyomark et al., 2009) and frequently used for the assessment of muscle fatigue in surface EMG signals (Cifrek et al., 2009).

This chapter presents a usefulness of MNF and MDF in electromyography analysis. The successful muscular fatigue assessment based on MNF and MDF methods is presented together with the principle and theory of MNF and MDF in this chapter, and also up-to-date literature reviews of MNF and MDF in the analysis of EMG signals. In order to analyse the EMG signals during dynamic movements, the effects of muscle force and muscle geometry

© 2012 Phinyomark et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Phinyomark et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

(joint angle) should have paid more attention (Cechetto et al., 2001; Doheny et al., 2008). In the literature, such effects on MNF and MDF have still been inconclusive (Doheny et al., 2008; Phinyomark et al., 2012c). A summary of the conflicting results mentioned in the literature is also presented. The possible reasons for the conflicting results in both effects are discussed. In addition to the clinical applications, the classification of EMG signals during upper-limb movements for using in the engineering applications (Oskoei & Hu, 2007) is proposed in this chapter.

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 197

<sup>=</sup> , (1)

= = . (2)

the central frequency (*fc*), centroid and the spectral center of gravity, in a number of studies (Du & Vuskovic, 2004; Farina & Merletti, 2000). In addition, MNF is also called as mean power frequency and mean spectral frequency in several works. The definition of MNF is

1 1

where *fj* is the frequency value of EMG power spectrum at the frequency bin *j*, *Pj* is the EMG power spectrum at the frequency bin *j*, and *M* is the length of frequency bin. In the analysis of EMG signal, *M* is usually defined as the next power of 2 from the length of EMG data in

MDF is a frequency at which the EMG power spectrum is divided into two regions with equal amplitude (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2012a). MDF is also defined as a half of the total power, or TTP (dividing the total power area into two equal parts). The

1 1

The behaviour of MNF and MDF is always similar. However, the performance of MNF in each of the applications is quite different compared to the performance of MDF, although both features are two kinds of averages in statistics. More details about the performance of

It should be noted that MNF is always slightly higher than MDF because of the skewed shape of EMG power spectrum (Knaflitz et al., 1990), whereas the variance of MNF is typically lower than that of MDF. In theory, the standard deviation of MDF is higher than that of MNF by a factor 1.253 (Balestra et al., 1988). However, the estimation of MDF is less affected by random noise, particularly in the case of noise located in the high frequency band of EMG power spectrum, and more affected by muscle fatigue (Stulen & De Luca,

**2.2. The relations between mean and median frequencies and other EMG** 

Other spectral variables that have been applied in the analysis of EMG signal are total power (TTP), mean power (MNP), peak frequency (PKF), the spectral moments (SM), frequency ratio (FR), power spectrum ratio (PSR), and variance of central frequency (VCF) (Phinyomark et al., 2012a). The definition of all variables is presented in the following.

1. TTP is an aggregate of EMG power spectrum (Phinyomark et al., 2012a). This feature is also defined as the energy and the zero spectral moment (SM0) (Du & Vuskovic, 2004).

*j jj j j MDF j P PP* == =

*MDF M M*

1 2

*M M jj j j j MNF f P P* = =

given by

time-domain.

1981).

definition of MDF is given by

**frequency-domain features** 

Its equation can be expressed as

both features are discussed in Section 3 to Section 6.

The rest of this chapter is as follows: Section 2 presents the principle and theory of MNF and MDF, and the relations between MNF (and MDF) and other EMG frequency-domain features are also described and discussed. In Section 3, the extensive review and careful survey of the up-to-date experiments for the assessing muscle fatigue using MNF and MDF in numerous applications are summarized, and moreover, the recent trend of MNF and MDF in the assessment of muscle fatigue is discussed in this section. On the other hand, the effects of muscle force and muscle geometry are described respectively in Section 4 and Section 5, with the re-evaluating results for both effects using the new EMG data set. In addition, a number of techniques that are possible to make the consistent results for both effects are suggested. In Section 6, the usefulness of MNF and MDF in the EMG pattern classification is proposed with the related works. The modified MNF and MDF in order to improve the robustness property for the classifying EMG signals are also presented. Lastly, the conclusion and future trends of using MNF and MDF to analyse EMG signals are presented in Section 7.
