Biomedical Engineering Reference
In-Depth Information
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-
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/
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.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.