Biomedical Engineering Reference
In-Depth Information
3. Sanchez, J.C., et al.,
A comparison between nonlinear mappings and linear state estimation to model
the relation from motor cortical neuronal firing to hand movements
, in SAB Workshop on Motor
Control in Humans and Robots: on the Interplay of Real Brains and Artificial Devices. 2002.
University of Edinburgh, Scotland.
4. 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
5. Wahba, G., Spline Models for Observational Data. 1990, Montpelier: Capital City Press.
6. Kim, S.-P., J.C. Sanchez, and J.C. Principe,
Real time input selection for linear time-variant
MIMO systems.
Optimization Methods and Software, 2007.
22
: pp. 83-98.
doi:10.1080/105
7. Sanchez, J.C., et al.,
Ascertaining the importance of neurons to develop better brain machine inter-
faces.
IEEE Transactions on Biomedical Engineering, 2003.
61
(6): pp. 943-953.
doi:10.1109/
8. Hadamard, J.P.U.B.,
Sur les problèmes aux dérivées partielles et leur signification physique
. Prince-
ton University Bulletin, 1902: pp. 49-52.
9. Tikhonov, A. and V. Arsenin, Solution of Ill-Posed Problems. 1977, Washington: Winston.
10. Neal, R., Bayesian Learning for Neural Networks. 1996, Cambridge: Cambridge University
Press.
11. Vapnik, V., The Nature of Statistical Learning Theory. Statistics for Engineering and Informa-
tion Science. 1999, New York: Springer-Verlag.
12. Stewart, G.W., Introduction to Matrix Computations. 1973, New York: Academic Press.
13. Klema, V.C. and A.J. Laub,
The singular value decomposition: Its computation and some applica-
tions.
IEEE Transactions on Automatic Control, 1980.
aC-25
: pp. 164-176.
doi:10.1109/
14. Haykin, S., Adaptive Filter Theory. 3rd ed. 1996, Upper Saddle River, NJ: Prentice-Hall
International.
15. Hoerl, A.E. and R.W. Kennard,
Ridge regression: Biased estimation for nonorthogonal problems.
Technometrics, 1970.
12
(3): pp. 55-67.
16. Weigend, A.S., D.E. Rumelhart, and B.A. Huberman,
Generalization by weight-elimination with
application to forecasting
. Advances in Neural Information Processing Systems 3. R.P. Lippmann,
J. Moody, and D.S. Touretzky, eds. 1991. pp. 875-882, Morgan Kaufmann: San Mateo, CA.
17. Larsen, J., et al.,
Adaptive regularization in neural network modeling
, Neural Networks: Tricks
of the Trade, G.B. Orr and K. Muller, eds., 1996, Germany: Springer, pp. 113-132.
doi:10.1007/
18. Geisser, S.,
The predictive sample reuse method with applications.
Journal of the American Statisti-
cal Association, 1975.
50
: pp. 320-328.
doi:10.2307/2285815
