**6. EMG pattern classification using mean and median frequencies**

EMG pattern classification is applied in several potential applications, particularly in the engineering context. The multifunction myoelectric control system (MMCS) is a main engineering application (Oskoei & Hu, 2007; Zecca et al., 2002) consisting the control of prosthesis, industrial robot arms, electric wheelchairs, virtual keyboard and virtual mouse. To be successful in the classification of EMG signals, three main cascaded modules should be carefully considered: data-preprocessing, feature extraction, and classification methods, especially the selection of feature extraction methods (Boostani & Moradi, 2003; Phinyomark et al., 2012a).

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 211

**Figure 6.** Scatter plots of (a) MNF and (b) MDF for 2 EMG channels (CH.A and CH.B) and 6 upper-limb movements (HO, HC, WE, WF, FP and FS) of a healthy subject. Note that CH.A is flexor carpi radialis

(b)

(a)

Further, in order to increase the robustness property of the MNF and MDF features, a modification of the MNF and MDF methods was proposed in one of our previous works (Phinyomark et al., 2009). The statistical variables (mean and median) were applied to the amplitude spectrum instead of the power spectrum, as used in the traditional methods, because the variation of the amplitude spectrum is less than that of the power spectrum. As a result, the variation of the modified MNF and MDF features is less than that of the traditional MNF and MDF features. These findings are confirmed using the real EMG data

Mean frequency (MNF) and median frequency (MDF) are two useful and popular frequency-domain features for electromyography analysis both in clinical and engineering applications. MNF and MDF are frequently used as the gold standard tool to detect fatigue in the target muscles using EMG signals. The effectiveness of MNF and MDF under many

muscle; CH.B is extensor carpi radialis longus muscle.

as presented in Phinyomark et al. (2009).

**7. Conclusion and future trends** 

Three criteria: maximum class separability, robustness and complexity, have been suggested and generally used to evaluate the EMG features for MMCS. Time-domain features have been usually used to make an optimal feature vector. However, only one feature per EMG channel is obtained from most of the frequency-domain methods (Boostani & Moradi, 2003), and their discriminant patterns in feature space are different from that of time-domain features (Phinyomark et al., 2012a). For a more powerful feature vector, an optimal frequency-domain feature should be combined with other successful time-domain features, such as the waveform length or WL (Oskoei & Hu, 2008), the RMS (Phinyomark et al., 2010), and the Willison amplitude or WAMP (Phinyomark et al., 2011).

First, the classification performance of the MNF and MDF features is discussed. Both features have the similar discriminant patterns, as can be observed from the scatter plots in Figs. 6(a) and 6(b). However, the MNF feature showed (a bit) better performance in class separation than the MDF feature. This can be confirmed by the classification accuracy obtained from the linear discriminant (LD) classifier. Mean and standard deviation of the classification accuracy obtained from MNF and MDF are 75.56±11.8% and 70.54±10.4%, respectively (Phinyomark et al., 2012a). Such classification accuracies are computed based on the classification of six upper-limb movements (hand open or HO, hand close or HC, wrist extension or WE, wrist flexion or WF, forearm pronation or FP, and forearm supination or FS) and five EMG channels (the extensor carpi radialis longus, the extensor carpi ulnaris, the extensor digitorum communis, the flexor carpi radialis, and the biceps brachii) from twenty healthy subjects (ten men and ten women).

For other frequency-domain features, five features consisting TTP, MNP, SM1, SM2 and SM3 have the same discriminant patterns in feature space as features in time-domain i.e. the RMS and the WL (Phinyomark et al., 2012a). Therefore, these features are not recommended to be one of the optimal features due to their higher computational cost. Moreover, the discriminant pattern of PSR is an inverse case of MNF and MDF, but the classification accuracy of PSR is less than that of MNF and MDF.

On the other hand, three features consisting PKF, VCF, and FR have the different discriminant patterns by comparing with MNF, MDF, and also time-domain features. The classification accuracies obtained from the classifier of PKF and VCF are very low (<50%), while the classification accuracy of FR (69.81%) is a bit less than that of MNF and MDF (Phinyomark et al., 2012a). Based on the results mentioned above, it can be concluded that MNF is an optimal frequency-domain feature for the EMG pattern classification.

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 211

**Figure 6.** Scatter plots of (a) MNF and (b) MDF for 2 EMG channels (CH.A and CH.B) and 6 upper-limb movements (HO, HC, WE, WF, FP and FS) of a healthy subject. Note that CH.A is flexor carpi radialis muscle; CH.B is extensor carpi radialis longus muscle.

Further, in order to increase the robustness property of the MNF and MDF features, a modification of the MNF and MDF methods was proposed in one of our previous works (Phinyomark et al., 2009). The statistical variables (mean and median) were applied to the amplitude spectrum instead of the power spectrum, as used in the traditional methods, because the variation of the amplitude spectrum is less than that of the power spectrum. As a result, the variation of the modified MNF and MDF features is less than that of the traditional MNF and MDF features. These findings are confirmed using the real EMG data as presented in Phinyomark et al. (2009).

#### **7. Conclusion and future trends**

Computational Intelligence in Electromyography Analysis – 210 A Perspective on Current Applications and Future Challenges

et al., 2012a).

**6. EMG pattern classification using mean and median frequencies** 

EMG pattern classification is applied in several potential applications, particularly in the engineering context. The multifunction myoelectric control system (MMCS) is a main engineering application (Oskoei & Hu, 2007; Zecca et al., 2002) consisting the control of prosthesis, industrial robot arms, electric wheelchairs, virtual keyboard and virtual mouse. To be successful in the classification of EMG signals, three main cascaded modules should be carefully considered: data-preprocessing, feature extraction, and classification methods, especially the selection of feature extraction methods (Boostani & Moradi, 2003; Phinyomark

Three criteria: maximum class separability, robustness and complexity, have been suggested and generally used to evaluate the EMG features for MMCS. Time-domain features have been usually used to make an optimal feature vector. However, only one feature per EMG channel is obtained from most of the frequency-domain methods (Boostani & Moradi, 2003), and their discriminant patterns in feature space are different from that of time-domain features (Phinyomark et al., 2012a). For a more powerful feature vector, an optimal frequency-domain feature should be combined with other successful time-domain features, such as the waveform length or WL (Oskoei & Hu, 2008), the RMS (Phinyomark et al., 2010),

First, the classification performance of the MNF and MDF features is discussed. Both features have the similar discriminant patterns, as can be observed from the scatter plots in Figs. 6(a) and 6(b). However, the MNF feature showed (a bit) better performance in class separation than the MDF feature. This can be confirmed by the classification accuracy obtained from the linear discriminant (LD) classifier. Mean and standard deviation of the classification accuracy obtained from MNF and MDF are 75.56±11.8% and 70.54±10.4%, respectively (Phinyomark et al., 2012a). Such classification accuracies are computed based on the classification of six upper-limb movements (hand open or HO, hand close or HC, wrist extension or WE, wrist flexion or WF, forearm pronation or FP, and forearm supination or FS) and five EMG channels (the extensor carpi radialis longus, the extensor carpi ulnaris, the extensor digitorum communis, the flexor carpi radialis, and the biceps

For other frequency-domain features, five features consisting TTP, MNP, SM1, SM2 and SM3 have the same discriminant patterns in feature space as features in time-domain i.e. the RMS and the WL (Phinyomark et al., 2012a). Therefore, these features are not recommended to be one of the optimal features due to their higher computational cost. Moreover, the discriminant pattern of PSR is an inverse case of MNF and MDF, but the classification

On the other hand, three features consisting PKF, VCF, and FR have the different discriminant patterns by comparing with MNF, MDF, and also time-domain features. The classification accuracies obtained from the classifier of PKF and VCF are very low (<50%), while the classification accuracy of FR (69.81%) is a bit less than that of MNF and MDF (Phinyomark et al., 2012a). Based on the results mentioned above, it can be concluded that

MNF is an optimal frequency-domain feature for the EMG pattern classification.

and the Willison amplitude or WAMP (Phinyomark et al., 2011).

brachii) from twenty healthy subjects (ten men and ten women).

accuracy of PSR is less than that of MNF and MDF.

Mean frequency (MNF) and median frequency (MDF) are two useful and popular frequency-domain features for electromyography analysis both in clinical and engineering applications. MNF and MDF are frequently used as the gold standard tool to detect fatigue in the target muscles using EMG signals. The effectiveness of MNF and MDF under many experimental conditions is presented and confirmed in this chapter, although the effects of muscle force and muscle geometry on MNF and MDF are inconclusive. However, the possible reasons for the conflicting results in both effects have been described and discussed in detail together with the possible techniques to make the consistent results for MNF and MDF with the both effects, as mentioned in the following.

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 213

Al-Mulla, M. R.; Sepulveda, F. and Colley, M. (2011). A Review of Non-invasive Techniques to Detect and Predict localised Muscle Fatigue. *Sensors*, Vol.11, No.4, pp. 3545-3594,

Al-Mulla, M. R.; Sepulveda, F. and Colley, M. (2012b). sEMG Techniques to Detect and Predict Localised Muscle Fatigue, In: *EMG Methods for Evaluating Muscle and Nerve Function*, Mark Schwartz, pp. 157-186, InTech, ISBN 978-953-307-793-2, Rijeka, Croatia Arnall, F. A.; Koumantakis, G. A.; Oldham, J. A. & Cooper, R. G. (2002). Between-days Reliability of Electromyographic Measures of Paraspinal Muscle Fatigue at 40, 50 and 60% Levels of Maximal Voluntary Contractile Force. *Clinical Rehabilitation*, Vol.16, No.7,

Balasubramanian, V.; Dutt, A. & Rai, S. (2011). Analysis of Muscle Fatigue in Helicopter

Balestra, G.; Knaflitz, M. & Merletti, R. (1988). Comparison between Myoelectric Signal Mean and Median Frequency Estimated. *Proceedings of EMBC 1988 Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, pp. 1708-1709, ISBN 0-

Bilodeau, M.; Arsenault, A. B.; Gravel, D. & Bourbonnais, D. (1991). EMG Power Spectra of Elbow Extensors during Ramp and Step Isometric Contractions. *European Journal of Applied Physiology and Occupational Physiology*, Vol.63, No.1, pp. 24-28, ISSN 0301-5548 Bilodeau, M.; Arsenault, A. B.; Gravel, D. & Bourbonnais, D. (1992). Influence of Gender on the EMG Power Spectrum during an Increasing Force Level. *Journal of Electromyography* 

Bilodeau, M.; Schindler-Ivens, S.; Williams, D. M.; Chandran, R. & Sharma, S. S. (2003). EMG Frequency Content Changes with Increasing Force and during Fatigue in the Quadriceps Femoris Muscle of Men and Women. *Journal of Electromyography and* 

Bonato, P.; Roy, S. H.; Knaflitz, M. & De Luca, C. J. (2001). Time Frequency Parameters of the Surface Myoelectric Signal for Assessing Muscle Fatigue during Cyclic Dynamic Contractions. *IEEE Transactions on Biomedical Engineering*, Vol.48, No.7, pp. 745-753,

Boostani, R. & Moradi, M. H. (2003). Evaluation of the Forearm EMG Signal Features for the Control of a Prosthetic Hand. *Physiological Measurement*, Vol.24, No.2, pp. 309-319, ISSN

Cechetto, A. D.; Parker, P. A. & Scott, R. N. (2001). The Effects of Four Time-varying Factors on the Mean Frequency of a Myoelectric Signal. *Journal of Electromyography and* 

Cifrek, M.; Medved, V.; Tonković, S. & Ostojić, S. (2009). Surface EMG based Muscle Fatigue Evaluation in Biomechanics. *Clinical Biomechanics*, Vol.24, No.4, pp. 327-340, ISSN 0268-

Pilots. *Applied Ergonomics*, Vol.42, No.6, pp. 913-918, ISSN 0003-6870

7803-0785-2, New Orleans, LA, USA, November 4-7, 1988

*and Kinesiology*, Vol.2, No.3, pp. 121-129, ISSN 1050-6411

*Kinesiology*, Vol.13, No.1, pp. 83-92, ISSN 1050-6411

*Kinesiology*, Vol.11, No.5, pp. 347-354, ISSN 1050-6411

ISSN 1424-8220

ISSN 0018-9294

0967-3334

0033

pp. 761-771, ISSN 0269-2155


However, the question remains whether the conflicting results, i.e. subject dependent, are found for the effect of both muscle force and muscle geometry on MNF and MDF. To address this question, two further works should be investigated: (1) finding the correlation between related anthropometric variables obtained from the subjects and MNF (or MDF), and (2) requesting all interested information to complete all components in Tables 2 and 3, and finding the possible reasons from the complete experimental conditions.

In total, MNF and MDF features extracted from the EMG signal are the optimal variables to identify muscle fatigue, particularly for static muscle contraction. However, for dynamic muscle contraction, applying instantaneous MNF and MDF are recommended.

The recommendations above can be useful to apply for most electromyography applications, such as human-computer interaction (HCI), ergonomics, occupational therapy and sport science. In addition, applying both techniques can make the MNF and MDF features to be the universal indices than can identify all factors including muscle force, muscle geometry, and muscle fatigue.
