References
- Hudgins, B., Englehart, K., Parker, P., and Scott, R., 1997, A microprocessor based multifunction Myoelectric control system. Proceedings of the 23rd Canadian Medical and Biological Engineering Society Conference, Toronto, May.
- Kumaravel, N., and Kavitha, V., 1994, Automatic diagnosis of neuro-muscular diseases using neural network. Biomedical Sciences Instru-mentation, 30, 245–250.
- Scott, R., and Parker, P., 1988, Myoelectric prostheses: State of the art. Journal of Medical Engineering & Technology, 12, 143 — 151.
- Englehart, K., and Hudgins, B., 2003, A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50, 848–854.
- Hudgins, B., Parker, P., and Scott, R., 1993, A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40, 82–94.
- Katutoshi, K., Koji, O., and Takao, T., 1992, A discrimination system using neural networks for EMG-controlled prostheses. Proceedings of IEEE Int. Workshop Robot Human Communications (IEEE), Tokyo, Japan, 69–74.
- Englehart, K., Hudgins, B., and Parker, P., 2001, A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomedical Engineering, 48, 302–311.
- Englehart, K., Hudgins, B., Parker, P., and Stevenson, M., 1999, Classification of the myoelectric signal using time-frequency based representations. Medical Engineering and Physics, 21, 431 —438.
- Kelly, M., Parker, P., and Scott, R., 1990, The application of neural networks to myoelectric signal analysis: a preliminary study. IEEE Trans Biomedical Engineering, 37, 221 —230.
- Kuruganti, U., Hudgins, B., and Scott, R., 1995, Two-channel enhancement of a multifunction control system. IEEE Transactions on Biomedical Engineering, 42, 109 — 111.
- Zalzala, A., and Chiyaratana, N., 2000, Myoelectric signal classifica-tion using evolutionary hybrid RBF-MLP networks. Proceedings of the 2000 Congress on Evolutionary Computation, 691 — 698. San Diego, CA, 2.
- Haykin, S., 1994, Neural Networks: A Comprehensive Foundation (New York: Macmillan).
- Halici, U., Erol, A., and Ongun, G., 1999, Industrial applications of hierarchical neural networks: Character recognition and finger print classification. In Industrial Applications of Neural Networks, Interna-tional Series on Computational Intelligence. Edited by L.C. JaM and V.R. Vemury, CRC Press, USA, pp. 159— 192.
- Qian, S., and Chen, D., 1996, Joint time-frequency analysis: Methods and Applications (Upper Saddle River, N.J.: Prentice Hall).
- Mallat, S., and Zhong, S., 1992, Characterization of signals for multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 710–732.
- Donoho, D., Huo, X., and Yu, T., 1999, Wavelab Soft-ware, Stanford University, http://www-stat.stanford.edu/ wavelab.
- Chaiyaratana, N., Zalzala, A., and Datta, D., 1996, Myoelectric signals pattern recognition for intelligent functional operation of upper-limb prosthesis. Proceedings of the 1st European Conference on Disability, Virtual Reality & Associated Technologies, ECOVRAT and University of Reading, UK, 151–160.
- Neurosolution software, 2000, Neural Network software, NeuroDi-mension, Inc, http://www.nd.com.