Neural decoding of movements: From linear to nonlinear trajectory models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4984 LNCS, Issue PART 1, 2008, Pages 586-595

  Yu, Byron M ^a,b   Cunningham, John P ^a   Shenoy, Krishna V ^a   Sahani, Maneesh ^b  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Expectation propagation; Gaussian quadrature; Neural decoding; Neural prosthetics; Nonlinear dynamical models; Nonlinear state estimation

Indexed keywords

FLUID STRUCTURE INTERACTION; MODELS; MOLECULAR VIBRATIONS; TRAJECTORIES; TRELLIS CODES;

EXPECTATION-PROPAGATION; GAUSSIAN QUADRATURE; NEURAL DECODING; NEURAL PROSTHETICS; NONLINEAR DYNAMICAL MODELS; NONLINEAR STATE ESTIMATION;

DECODING;

EID: 54249147446     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69158-7_61     Document Type: Conference Paper

References (37)

1. Brown, E.N., Frank, L.M., Tang, D., Quirk, M.C., Wilson, M.A.: A statistical paradigm for neural spike train decoding applied to position prediction from the ensemble firing patterns of rat hippocampal place cells. J. Neurosci 18(18), 7411-7425 (1998)
2. Zhang, K., Ginzburg, I., McNaughton, B.L., Sejnowski, T.J.: Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells. J. Neurophysiol 79, 1017-1044 (1998)
3. Wessberg, J., Stambaugh, C.R., Kralik, J.D., Beck, P.D., Laubach, M., Chapin, J.K., Kim, J., Biggs, J., Srinivasan, M.A., Nicolelis, M.A.L.: Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810), 361-365 (2000)
4. Schwartz, A.B., Taylor, D.M., Tillery, S.I.H.: Extraction algorithms for cortical control of arm prosthetics. Curr. Opin. Neurobiol. 11, 701-707 (2001)
5. Serruya, M., Hatsopoulos, N., Fellows, M., Paninski, L., Donoghue, J.: Robustness of neuroprosthetic decoding algorithms. Biol. Cybern. 88(3), 219-228 (2003)
6. Brockwell, A.E., Rojas, A.L., Kass, R.E.: Recursive Bayesian decoding of motor cortical signals by particle filtering. J. Neurophysiol 91(4), 1899-1907 (2004)
7. Wu, W., Black, M.J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J.P.: Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Trans Biomed Eng 51(6), 933-942 (2004)
8. Yu, B.M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S.I., Meng, T.H., Sahani, M., Shenoy, K.V.: Mixture of trajectory models for neural decoding of goal-directed movements. J. Neurophysiol. 97, 3763-3780 (2007)
9. Chapin, J.K., Moxon, K.A., Markowitz, R.S., Nicolelis, M.A.L.: Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664-670 (1999)
10. Serruya, M.D., Hatsopoulos, N.G., Paninski, L., Fellows, M.R., Donoghue, J.P.: Instant neural control of a movement signal 416, 141-142 (2002)
11. Taylor, D.M., Tillery, S.I.H., Schwartz, A.B.: Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829-1832 (2002)
12. Carmena, J.M., Lebedev, M.A., Crist, R.E., O'Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., Nicolelis, M.A.L.: Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology 1(2), 193-208 (2003)
13. Musallam, S., Corneil, B.D., Greger, B., Scherberger, H., Andersen, R.A.: Cognitive control signals for neural prosthetics. Science 305, 258-262 (2004)
14. Santhanam, G., Ryu, S.I., Yu, B.M., Afshar, A., Shenoy, K.V.: A high-performance brain-computer interface. Nature 442, 195-198 (2006)
15. Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., Donoghue, J.P.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164-171 (2006)
16. Wu, W., Gao, Y., Bienenstock, E., Donoghue, J.P., Black, M.J.: Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput 18(1), 80-118 (2006)
17. Kemere, C., Meng, T.: Optimal estimation of feed-forward-controlled linear systems. In: Proc IEEE ICASSP, pp. 353-356 (2005)
18. Srinivasan, L., Eden, U.T., Willsky, A.S., Brown, E.N.: A state-space analysis for reconstruction of goal-directed movements using neural signals. Neural Comput 18(10), 2465-2494 (2006)
19. Srinivasan, L., Brown, E.N.: A state-space framework for movement control to dynamic goals through brain-driven interfaces. IEEE Trans. Biomed. Eng. 54(3), 526-535 (2007)
20. Shoham, S., Paninski, L.M., Fellows, M.R., Hatsopoulos, N.G., Donoghue, J.P., Normann, R.A.: Statistical encoding model for a primary motor cortical brainmachine interface. IEEE Trans. Biomed. Eng. 52(7), 1313-1322 (2005)
21. Wan, E., van der Merwe, R.: The unscented Kalman filter. In: Haykin, S. (ed.) Kalman Filtering and Neural Networks, Wiley Publishing, Chichester (2001)
22. Julier, S., Uhlmann, J.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401-422 (2004)
23. Arasaratnam, I., Haykin, S., Elliott, R.: Discrete-time nonlinear filtering algorithms using Gauss-Hermite quadrature. Proceedings of the IEEE 95(5), 953-977 (2007)
24. Minka, T.: Expectation propagation for approximate Bayesian inference. In: Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 362-369 (2001)
25. Heskes, T., Zoeter, O.: Expectation propagation for approximate inference in dynamic Bayesian networks. In: Darwiche, A., Friedman, N. (eds.) Proceedings UAI-2002, pp. 216-223 (2002)
26. Zoeter, O., Ypma, A., Heskes, T.: Improved unscented Kalman smoothing for stock volatility estimation. In: Barros, A., Principe, J., Larsen, J., Adali, T., Douglas, S. (eds.) Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (2004)
27. Ypma, A., Heskes, T.: Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing 69, 85-99 (2005)
28. Yu, B.M., Shenoy, K.V., Sahani, M.: Expectation propagation for inference in nonlinear dynamical models with Poisson observations. In: Proc. IEEE Nonlinear Statistical Signal Processing Workshop (2006)
29. Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
30. van der Merwe, R., Wan, E.: Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. In: Proceedings of the Workshop on Advances in Machine Learning (2003)
31. Kotecha, J.H., Djuric, P.M.: Gaussian particle filtering. IEEE Transactions on Signal Processing 51(10), 2592-2601 (2003)
32. MacKay, D.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)
33. Smola, A., Vishwanathan, V., Eskin, E.: Laplace propagation. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2004)
34. Minka, T., Lafferty, J.: Expectation-propagation for the generative aspect model. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 352-359 (2002)
35. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197-208 (2000)
36. Neal, R.M., Beal, M.J., Roweis, S.T.: Inferring state sequences for non-linear systems with embedded hidden Markov models. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2004)
37. Chan, S.S., Moran, D.W.: Computational model of a primate arm: from hand position to joint angles, joint torques and muscle forces. J. Neural. Eng. 3, 327-337 (2006)