Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 685492, 11 pages
http://dx.doi.org/10.1155/2014/685492
Research Article

Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

1Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
2Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
3Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China

Received 28 February 2014; Accepted 17 April 2014; Published 18 May 2014

Academic Editor: Ting Zhao

Copyright © 2014 Kai Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. K. Chapin, K. A. Moxon, R. S. Markowitz, and M. A. L. Nicolelis, “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,” Nature Neuroscience, vol. 2, no. 7, pp. 664–670, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. P. R. Kennedy, R. A. E. Bakay, M. M. Moore, K. Adams, and J. Goldwaithe, “Direct control of a computer from the human central nervous system,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 198–202, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows, and J. P. Donoghue, “Instant neural control of a movement signal,” Nature, vol. 416, no. 6877, pp. 141–142, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. D. M. Taylor, S. I. H. Tillery, and A. B. Schwartz, “Direct cortical control of 3D neuroprosthetic devices,” Science, vol. 296, no. 5574, pp. 1829–1832, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. L. R. Hochberg, D. Bacher, B. Jarosiewicz et al., “Reach and grasp by people with tetraplegia usinga neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, 2012. View at Google Scholar
  6. R. B. Stein, P. H. Peckham, and D. B. Popovi, Eds., Neural Prostheses: Replacing MotOr Function After Disease Or Disability, Oxford University Press, 1992.
  7. M. A. Lebedev and M. A. L. Nicolelis, “Brain-machine interfaces: past, present and future,” Trends in Neurosciences, vol. 29, no. 9, pp. 536–546, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. J. P. Donoghue, “Bridging the brain to the world: a perspective on neural interface systems,” Neuron, vol. 60, no. 3, pp. 511–521, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Wessberg, C. R. Stambaugh, J. D. Kralik et al., “Real-time prediction of hand trajectory by ensembles of cortical neurons in primates,” Nature, vol. 408, no. 6810, pp. 361–365, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. J. M. Carmena, M. A. Lebedev, R. E. Crist et al., “Learning to control a brain-machine interface for reaching and grasping by primates,” PLoS Biology, vol. 1, no. 2, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Velliste, S. Perel, M. C. Spalding, A. S. Whitford, and A. B. Schwartz, “Cortical control of a prosthetic arm for self-feeding,” Nature, vol. 453, no. 7198, pp. 1098–1101, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. J. C. Sanchez, S. P. Kim, D. Erdogmus et al., “Input-output mapping performance oflinear and nonlinear models for estimating hand trajectories from cortical neuronal firingpatterns,” in Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, vol. 2002, pp. 139–148.
  13. S.-P. Kim, J. C. Sanchez, D. Erdogmus et al., “Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models,” Neural Networks, vol. 16, no. 5-6, pp. 865–871, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. A. E. Brockwell, A. L. Rojas, and R. E. Kass, “Recursive bayesian decoding of motor cortical signals by particle filtering,” Journal of Neurophysiology, vol. 91, no. 4, pp. 1899–1907, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Wu, M. J. Black, D. Mumford, Y. Gao, E. Bienenstock, and J. P. Donoghue, “Modeling and decoding motor cortical activity using a switching Kalman filter,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 933–942, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Wu, Y. Gao, E. Bienenstock, J. P. Donoghue, and M. J. Black, “Bayesian population decoding of motor cortical activity using a Kalman filter,” Neural Computation, vol. 18, no. 1, pp. 80–118, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Srinivasan, U. T. Eden, S. K. Mitter, and E. N. Brown, “General-purpose filter design for neural prosthetic devices,” Journal of Neurophysiology, vol. 98, no. 4, pp. 2456–2475, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Xu, Y. Wang, S. Zhang et al., “Comparisons between linear and nonlinear methods for decoding motor cortical activities of monkey,” in Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '11), pp. 4207–4210, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Ergun, R. Barbieri, U. T. Eden et al., “Construction of point process adaptive filter algorithmsfor neural systems using sequential Monte Carlo methods,” IEEETransactions on Biomedical Engineering, vol. 54, no. 3, pp. 419–428, 2007. View at Publisher · View at Google Scholar
  20. Y. Wang, A. R. C. Paiva, J. C. Príncipe, and J. C. Sanchez, “Sequential Monte Carlo point-process estimation of kinematics from neural spiking activity for brain-machine interfaces,” Neural computation, vol. 21, no. 10, pp. 2894–2930, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Wang and J. C. Principe, “Point process modeling on decoding and encoding for brain machine interfaces,” in Proceedings of the 7th Asian Control Conference (ASCC '09), pp. 1000–1005, August 2009. View at Scopus
  22. A. C. Smith and E. N. Brown, “Estimating a state-space model from point process observations,” Neural Computation, vol. 15, no. 5, pp. 965–991, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. U. T. Eden, L. M. Frank, R. Barbieri, V. Solo, and E. N. Brown, “Dynamic analysis of neural encoding by point process adaptive filtering,” Neural Computation, vol. 16, no. 5, pp. 971–998, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. W. Truccolo, U. T. Eden, M. R. Fellows, J. P. Donoghue, and E. N. Brown, “A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects,” Journal of Neurophysiology, vol. 93, no. 2, pp. 1074–1089, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. W. Truccolo, L. R. Hochberg, and J. P. Donoghue, “Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes,” Nature Neuroscience, vol. 13, no. 1, pp. 105–111, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. J. W. Pillow, J. Shlens, L. Paninski et al., “Spatio-temporal correlations and visual signalling in a complete neuronal population,” Nature, vol. 454, no. 7207, pp. 995–999, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Wu, J. E. Kulkarni, N. G. Hatsopoulos, and L. Paninski, “Neural decoding of hand motion using a linear state-space model with hidden states,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 4, pp. 370–378, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. V. Lawhern, W. Wu, N. Hatsopoulos, and L. Paninski, “Population decoding of motor cortical activity using a generalized linear model with hidden states,” Journal of Neuroscience Methods, vol. 189, no. 2, pp. 267–280, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. F. Zhou, J. Liu, Y. Yu et al., “Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats,” Journal of Neuroscience Methods, vol. 185, no. 2, pp. 299–306, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. X. Zhu, R. Jiang, Y. Chen, S. Hu, and D. Wang, “FPGA implementation of Kalman filter for neural ensemble decoding of rat's motor cortex,” Neurocomputing, vol. 74, no. 17, pp. 2906–2913, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. D. Wang, Y. Hao, X. Zhu et al., “FPGA implementation of hardware processing modules as coprocessors in brain-machine interfaces,” in Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '11), pp. 4613–4616, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. J. A. Wilson and J. C. Williams, “Massively parallel signal processing using the graphics processing unit for real-time brain-computer interface feature extraction,” Frontiers in Neuroengineering, vol. 2, no. 11, 2009. View at Publisher · View at Google Scholar
  33. M. A. L. Nicolelis, D. Dimitrov, J. M. Carmena et al., “Chronic, multisite, multielectrode recordings in macaque monkeys,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 19, pp. 11041–11046, 2003. View at Publisher · View at Google Scholar · View at Scopus
  34. Q. S. Zhang, S. M. Zhang, Y. Y. Hao et al., “Development of an invasive brain-machine interface witha monkey model,” Chinese Science Bulletin, vol. 57, no. 16, pp. 2036–2045, 2012. View at Google Scholar
  35. K. Xu, Y. Wang, Y. Wang et al., “Local-learning-based neuron selection for grasping gesture predictionin motor brain machine interfaces,” Journal of Neural Engineering, vol. 10, no. 2, Article ID 026008, 2013. View at Publisher · View at Google Scholar
  36. E. N. Brown, R. Barbieri, V. Ventura, R. E. Kass, and L. M. Frank, “The time-rescaling theorem and its application to neural spike train data analysis,” Neural Computation, vol. 14, no. 2, pp. 325–346, 2002. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Doucet, Sequential Monte Carlo Methods, John Wiley & Sons, 2001.
  38. A. Doucet, S. Godsill, and C. Andrieu, “On sequential monte carlo sampling methods for Bayesian filtering,” Statistics and Computing, vol. 10, no. 3, pp. 197–208, 2000. View at Google Scholar · View at Scopus
  39. R. Douc, O. Cappé, and E. Moulines, “Comparison of resampling schemes for particle filtering,” in Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA '05), pp. 64–69, September 2005. View at Scopus
  40. M. Harris, S. Sengupta, and J. D. Owens, “Parallel prefix sum (scan) with CUDA,” GPU Gems, vol. 3, no. 39, pp. 851–876, 2007. View at Google Scholar