Biomedical Engineering Reference
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30.
Kim, S.P., et al., Divide-and-conquer approach for brain machine interfaces: Nonlinear mixture of
competitive linear models . Neural Networks, 2003. 16 (5-6): pp. 865-871. doi:10.1016/S0893-
6080(03)00108-4
Hoerl, A.E., and R.W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems .
Technometrics, 1970. 12 (3): pp. 55-67.
Widrow, B., and S.D. Stearns, Adaptive Signal Processing. Prentice-Hall Signal Processing
Series. 1985, Englewood Cliffs, NJ: Prentice-Hall.
Príncipe, J.C., N.R. Euliano, and W.C. Lefebvre, Neural and Adaptive Systems: Fundamentals
Through Simulations. 2000, New York: Wiley.
Sanchez, J.C., et al. Interpreting neural activity through linear and nonlinear models for brain ma-
chine interfaces , in International Conference of Engineering in Medicine and Biology Society.
2003. Cancun, Mexico. doi:10.1109/IEMBS.2003.1280168
Rao, Y.N., et al. Learning mappings in brain-machine interfaces with echo state networks , in In-
ternational Joint Conference on Neural Networks. 2004. Budapest, Hungary. doi:10.1109/
ICASSP.2005.1416283
Sandberg, I.W., and L. Xu, Uniform approximation of multidimensional myopic maps . IEEE
Transactions on Circuits and Systems, 1997. 44 : pp. 477-485.
Todorov, E., On the role of primary motor cortex in arm movement control , in Progress in Motor
Control III, M. Latash, and M. Levin, eds. 2003, Urbana, IL: Human Kinetics.
Puskorius, G.V., et al., Dynamic neural network methods applied to on-vehicle idle speed control .
Proceedings of the IEEE, 1996. 4 (10): pp. 1407-1420. doi:10.1109/5.537107
Werbos, P.J., Backpropagation through time: What it does and how to do it . Proceedings of the
IEEE, 1990. 7 (10): pp. 1550-1560. doi:10.1109/5.58337
Lefebvre, W.C., et al., NeuroSolutions. 1994, Gainesville, FL: NeuroDimension.
Vapnik, V., The Nature of Statistical Learning Theory. Statistics for Engineering and Informa-
tion Science. 1999, New York: Springer-Verlag. 304.
Jaeger, H., The “Echo State” Approach to Analyzing and Training Recurrent Neural Networks ,
GMD Report 148. 2001, Sankt Augustin, Germany: GMD-German National Research Insti-
tute for Computer Science.
Maas, W., T. Natschläger, and H. Markram, Real-time computing without stable states: A new
framework for neural computation based on perturbations . Neural Computation, 2002. 14 (11):
pp. 2531-2560. doi:10.1162/089976602760407955
Principe, J.C., B. De Vries, and P.G. Oliveira, The gamma filter—A new class of adaptive IIR fil-
ters with restricted feedback . IEEE Transactions on Signal Processing, 1993. 41 (2): pp. 649-656.
doi:10.1109/78.193206
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
 
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