Predicting EMG data from M1 neurons with variational Bayesian least squares

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 1361-1368

  Ting, Jo Anne ^a   D'Souza, Aaron ^a   Yamamoto, Kenji ^c   Yoshioka, Toshinori ^b   Hoffman, Donna ^c   Kakei, Shinji ^d   Sergio, Lauren ^f   Kalaska, John ^e   Kawato, Mitsuo ^b   Strick, Peter ^c   Schaal, Stefan ^a,b  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^c UNIVERSITY OF PITTSBURGH   (United States)

^d TOKYO METROPOLITAN INSTITUTE OF MEDICAL SCIENCE   (Japan)

^e UNIVERSITÉ DE MONTRÉAL   (Canada)

^f YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARM MOVEMENTS; ARTIFICIAL DEVICES; BAYESIAN APPROACHES; BAYESIAN METHODS; BRAIN MACHINE INTERFACE; BRUTE FORCE; COMBINATORIAL SEARCH; EASE-OF-USE; HIGH DIMENSIONAL DATA; HIGH DIMENSIONS; INPUT FEATURES; LEAST SQUARE; LINEAR ANALYSIS TECHNIQUE; LINEAR APPROACH; LINEAR REGRESSION TECHNIQUES; NEURAL ACTIVITY; NEURAL FIRING; ORDINARY LEAST SQUARES; OVERFITTING; PARTIAL LEAST SQUARE (PLS); PRIMARY MOTOR CORTEX; REAL TIME INTERFACES; STEPWISE REGRESSION; VARIATIONAL BAYESIAN;

BAYESIAN NETWORKS; NEURONS;

LINEAR REGRESSION;

EID: 84864044059     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. M.A. Nicolelis. Actions from thoughts. Nature, 409:403-407, 2001.
2. D.M. Taylor, S.I. Tillery, and A.B. Schwartz. Direct cortical control of 3d neuroprosthetic devices. Science, 296:1829-1932, 2002.
3. J.R. Wolpaw and D.J. McFarland. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences, 101:17849-17854, 2004.
4. Y. Kamitani and F. Tong. Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8:679, 2004.
5. J.D. Haynes and G. Rees. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience, 8:686, 2005.
6. J. Wessberg and M.A. Nicolelis. Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys. Journal of Cognitive Neuroscience, 16:1022-1035, 2004.
7. S. Musallam, B.D. Corneil, B. Greger, H. Scherberger, and R.A. Andersen. Cognitive control signals for neural prosthetics. Science, 305:258-262, 2004.
8. R.M. Neal. Bayesian learning for neural networks. PhD thesis, Dept. of Computer Science, University of Toronto, 1994.
9. L.E. Sergio and J.F. Kalaska. Changes in the temporal pattern of primary motor cortex activity in a directional isometric force versus limb movement task. Journal of Neurophysiology, 80:1577-1583, 1998.
10. S. Kakei, D.S. Hoffman, and P.L. Strick. Muscle and movement representations in the primary motor cortex. Science, 285:2136-2139, 1999.
11. S. Kakei, D.S. Hoffman, and P.L. Strick. Direction of action is represented in the ventral premotor cortex. Nature Neuroscience, 4:1020-1025, 2001.
12. E. Todorov. Direct cortical control of muscle activation in voluntary arm movements: a model. Nature Neuroscience, 3:391-398, 2000.
13. N. R. Draper and H. Smith. Applied Regression Analysis. Wiley, 1981.
14. S. Derksen and H.J. Keselman. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology, 45:265-282, 1992.
15. W.F. Massey. Principal component regression in exploratory statistical research. Journal of the American Statistical Association, 60:234-246, 1965.
16. S. Schaal, S. Vijayakumar, and C.G. Atkeson. Local dimensionality reduction. In M.I. Jordan, M.J. Kearns, and S.A. Solla, editors, Advances in Neural Information Processing Systems. MIT Press, 1998.
17. I.E. Frank and J.H. Friedman. A statistical view of some chemometric regression tools. Technometrics, 35:109-135, 1993.
18. H. Wold. Soft modeling by latent variables: The nonlinear iterative partial least squares approach. In J. Gani, editor, Perspectives in probability and statistics, papers in honor of M. S. Bartlett. Academic Press, 1975.
19. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of Royal Statistical Society, Series B, 58(1):267-288, 1996.
20. V. Strassen. Gaussian elimination is not optimal. Num Mathematik, 13:354-356, 1969.
21. T. J. Hastie and R. J. Tibshirani. Generalized additive models. Number 43 in Monographs on Statistics and Applied Probability. Chapman and Hall, 1990.
22. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Society. Series B, 39(1):1-38, 1977.
23. A. D'Souza, S. Vijayakumar, and S. Schaal. The bayesian backfitting relevance vector machine. In Proceedings of the 21st International Conference on Machine Learning. ACM Press, 2004.
24. A. Gelman, J. Carlin, H.S. Stern, and D.B. Rubin. Bayesian Data Analaysis. Chapman and Hall, 2000.
25. Z. Ghahramani and M.J. Beal. Graphical models and variational methods. In D. Saad and M. Opper, editors, Advanced Mean Field Methods - Theory and Practice. MIT Press, 2000.