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The Scientific World Journal
Volume 2012, Article ID 478952, 5 pages
http://dx.doi.org/10.1100/2012/478952
Research Article

Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra

Department of Electrical and Electronics Engineering, Faculty of Architecture and Engineering, Batman University, 72060 Batman, Turkey

Received 2 September 2012; Accepted 12 October 2012

Academic Editors: T. Arendt and J. Benito-Leon

Copyright © 2012 Necmettin Sezgin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. Nazarpour, A. R. Sharafat, and S. M. P. Firoozabadi, “Surface EMG signal classification using a selective mix of higher order statistics,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 4208–4211, September 2005. View at Scopus
  2. A. Phinyomark, S. Hirunviriya, C. Limsakul, and P. Phukpattaranont, “Evaluation of EMG feature extraction for hand movement recognition based on euclidean distance and standard deviation,” in Proceedings of the 7th Annual International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON '10), pp. 856–860, May 2010. View at Scopus
  3. Y. Al-Assaf, “Surface myoelectric signal analysis: dynamic approaches for change detection and classification,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 11, pp. 2248–2256, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Gao, J. Lei, Q. Song, Y. Yu, and Y. Ge, “Research on the surface EMG signal for human body motion recognizing based on arm wrestling robot,” in Proceedings of the IEEE International Conference on Information Acquisition (ICIA '06), pp. 1269–1273, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Englehart, B. Hudgins, P. Parker, and M. Stevenson, “Time-frequency representation for classification of the transient myoelectric signal,” in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, no. 5, pp. 2627–2630, November 1998. View at Scopus
  6. X. Zhang, Y. Yang, X. Xu, and M. Zhang, “Wavelet-based neuro-fuzzy classification for EMG control,” in Proceedings of the IEEE International Conference on Communications, Circuits and Systems and West Sino Expositions, vol. 2, pp. 1087–1089, 2002.
  7. Z. Xizhi, “Study of surface electromyography signal based on wavelet transform and radial basis function neural network,” in Proceedings of the International Seminar on Future BioMedical Information Engineering (FBIE '08), pp. 160–163, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Bodruzzaman, M. Wilkes, R. Shiavi, and A. Kilroy, “Classification of electromyographic signals by autoregressive modeling,” in Proceedings of the IEEE Southeastcon on Technologies Today and Tomorrow, vol. 2, pp. 508–510, April 1990. View at Scopus
  9. Z. Jingdong, X. Zongwu, J. Li, C. Hegao, L. Hong, and G. Hirzinger, “EMG control for a five-fingered prosthetic hand based on wavelet transform and autoregressive model,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '06), pp. 1097–1102, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Murugappan, “Electromyogram signal based human emotion classification using KNN and LDA,” in Proceedings of the IEEE International Conference on System Engineering and Technology, pp. 106–110, 2011.
  11. A. Frank and A. Asuncion, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2010.
  12. J. C. Sigl and N. G. Chamoun, “An introduction to bispectral analysis for the electroencephalogram,” Journal of Clinical Monitoring, vol. 10, no. 6, pp. 392–404, 1994. View at Publisher · View at Google Scholar · View at Scopus
  13. M. J. Hinich and C. S. Clay, “The application of the discrete Fourier transform in the estimation of power spectra, coherence and bispectra of geophysical data,” Reviews of Geophysics, vol. 6, no. 3, pp. 347–363, 1968. View at Publisher · View at Google Scholar
  14. C. L. Nikias and A. P. Petropulu, Higher-Order Spectral Analysis: A Nonlinear Signal Processing Framework, Prentice-Hall, Engle-wood Cliffs, NJ, USA, 1993.
  15. C. L. Nikias and M. R. Raghuveer, “Bispectrum estimation: a digital signal processing framework,” Proceedings of the IEEE, vol. 75, no. 7, pp. 869–891, 1987. View at Google Scholar · View at Scopus
  16. M. R. Raghuveer and C. L. Nikias, “Bispectrum estimation: a parametric approach,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 33, no. 5, pp. 1213–1230, 1985. View at Google Scholar · View at Scopus
  17. T. Ning and J. D. Bronzino, “Bispectral analysis of the rat EEG during various vigilance states,” IEEE Transactions on Biomedical Engineering, vol. 36, no. 4, pp. 497–499, 1989. View at Google Scholar · View at Scopus
  18. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus