**1. Introduction**

Computational Intelligence in Electromyography Analysis – 174 A Perspective on Current Applications and Future Challenges

260.

Wiley &Sons Ltd. 337.

Processing. 1997.

17: 953-954.

Uspekhi, 2007. 50(8): 819-834.

Medicine and Biology Society. 1997.

Speech, and Signal Processing. 1997.

1988, Beijing: Mechanical Industry Press (in Chinese).

[63] B B Mandelbrot, Self-affine fractals and fractal dimension. Physica Scripta, 1985. 32: 257-

[65] K Falconer, Fractal Geometry: Mathematical Foundations and Applications. 2003: John

[66] B D Malamud and D L Turcotte, Self-affine time series: measures of weak and strong

[67] A N Pavlov and V S Anishchenko, Multifractal analysis of complex signals. Physics-

[68] R H Riedi, M S Crouse, V J Ribeiro, et al., A multifractal wavelet model with application

[69] A Z R Langi, K Soemintapura, and W Kinsner. Multifractal processing of speech signals. in International Conference on Information, Communications and Signal

[70] Z Xu and S Xiao. Fractal dimension of surface EMG and its determinants. in Proceedings of the 19th Annual International Conference of the IEEE Engineerings in

[71] O Adeyemi and G F Boudreaux-Bartels. Improved accuracy in the singularity spectrum of multifractal chaotic time series. in IEEE International Conference on Acoustics,

[72] C J Aumuth, Fractals dimension of electromyographic signals recorded with surface electrodes during isometric contractions with muscle activation. Muscle & Nerve, 1994.

[73] J Huang, The practical modeling method of static and dynamic mathematical model.

[64] B B Mandelbrot, The fractal geometry of Nature. 1982: San Francisco: Freeman.

persistence. Journal of Statistical Planning and Inference, 1999. 80: 173-196.

to network traffic. IEEE Trans. On Information Theory, 1999. 45(3): 992-1018.

Electromyography (EMG) has been around since the 1600s [1]. It is a tool used to measure the action potentials of motor units in muscles [2]. The EMG electrodes are like little microphones which "listen" for muscle action potentials so having these microphones in different locations relative to the muscle or motor units affects the nature of the recording [3]. The amplitude and frequency characteristics of the raw electromyogram signal have been shown to be highly variable and sensitive to many factors. De Luca [4] provided a detailed account of these characteristics which have a "basic" or "elemental" effect on the signal dividing them into extrinsic and intrinsic sub-factors. Extrinsic factors are those which can be influenced by the experimenter, and include: electrode configuration (distance between electrodes as well as area and shape of the electrodes); electrode placement with respect to the motor points in the muscle and lateral edge of the muscle as well as the orientation to the muscle fibres; skin preparation and impedance [5, 6]; and perspiration and temperature [7]. Intrinsic factors include: physiological, anatomical and biochemical characteristics of the muscles such as the number of active motor units; fiber type composition of the muscles; blood flow in the muscle; muscle fiber diameter; the distance between the active fibers within the muscle with respect to the electrode; and the amount of tissue between the surface of the muscle and the electrode. These factors vary between individuals, between days within an individual and within a day in an individual if the electrode set up has been altered. Given that there are many factors that influence the EMG signal, voltage recorded from a muscle is difficult to describe in terms of level if there is no reference value to which it can be compared. Therefore, interpretation of the amplitude of the raw EMG signal is problematic unless some kind of normalization procedure is performed. Normalization refers to the conversion of the signal to a scale relative to a known and repeatable value. It has been reported [8] that normalized EMG signals were first presented by Eberhart, Inman & Bresler in 1954 [9]. Since then, there have been a number of methods used to normalize EMG signals with no consensus as to which method is most

© 2012 Halaki and Ginn, 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 Halaki and Ginn, 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.

appropriate [8]. In this chapter, we will outline when the presentation of raw EMG is acceptable and when normalization is essential as well as the various methods used to normalize EMG signals. A discussion of the advantages and disadvantages of each method and examples of its uses will be provided.

Normalization of EMG Signals: To Normalize or Not to Normalize and What to Normalize to? 177

17, 18]. Normalization of EMG signals is usually performed by dividing the EMG signals during a task by a reference EMG value obtained from the same muscle. By normalizing to a reference EMG value collected using the same electrode configuration, factors that affect the EMG signals during the task and the reference contraction are the same. Therefore, one can

The common consensus is that a "good" reference value to which to normalize EMG signals should have high repeatability, especially in the same subject in the same session, and be meaningful. By choosing a reference value repeatable within an individual, one can compare the levels obtained from any task to that reference value. The choice of reference value should allow comparisons between individuals and between muscles. To be able to do so, the reference value should have similar meaning between individuals and between muscles. The choice of normalization method is critical in the interpretation of the EMG signals as it will influence the amplitude and pattern of the EMG signals [8]. Unfortunately, there is no consensus as to a single "best" method for normalization of EMG data [8, 18] and a variety

validly obtain a relative measure of the activation compared to the reference value.

of methods have been used to obtain normalization reference values: 1. Maximum (peak) activation levels during maximum contractions

3. Activation levels during submaximal isometric contractions 4. Peak to peak amplitude of the maximum M-wave (M-max)

*3.1.1. Maximal voluntary isometric contractions* 

neural activation capacity of the muscle [24-26].

2. Peak or mean activation levels obtained during the task under investigation

**3.1. Maximum (peak) activation levels during maximum contractions** 

The most common method of normalizing EMG signals from a given muscle uses to the EMG recorded from the same muscle during a maximal voluntary isometric contraction (MVIC) as the reference value [19-23]. The process of normalization using MVICs is that a reference test (usually a manual muscle test) is identified which produces a maximum contraction in the muscle of interest. Based on the repeatability between tests measures, it is recommended that at least 3 repetitions of the test be performed separated by at least 2 minutes to reduce any fatigue effects [12]. The EMG signals are then processed either by high-pass filtering, rectifying and smoothing or by calculating the root mean square of the signal. The maximum value obtained [12] from the processed signals during all repetitions of the test is then used as the reference value for normalizing the EMG signals, processed in the same way, from the muscle of interest. This allows the assessment of the level of activity of the muscle of interest during the task under investigation compared to the maximal

This method sounds simple enough. However, when trying to implement it, investigators are faced with an important question: *What test should be used to produce maximum neural activation in a given muscle?* The choice of MVIC should reflect the maximal neural activation capacity of the given muscle [27]. Unfortunately, there is no consensus as to which test produces maximal activation in all individuals in any given muscle. Table 1 provides some
