Dynamic analysis of naive adaptive brain-machine interfaces

Neural Computation

Volumn 25, Issue 9, 2013, Pages 2373-2420

  Kowalski, Kevin C ^a,b   He, Bryan D ^a,b   Srinivasan, Lakshminarayan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; BRAIN COMPUTER INTERFACE; FEEDBACK SYSTEM; HUMAN; LETTER;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BRAIN-COMPUTER INTERFACES; FEEDBACK; HUMANS;

EID: 84887556358     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00484     Document Type: Letter

References (50)

1. Barber, D. (2011). Bayesian reasoning and machine learning. Cambridge, New York: Cambridge University Press.
2. Bertsekas, D. P. (2005). Dynamic programming and optimal control (3rd ed.). Belmont, MA: Athena Scientific.
3. Blumenfeld, H. (2002). Neuroanatomy through clinical cases. Sunderland, MA: Sinauer.
4. Bradberry, T. J., Gentili, R. J., & Contreras-Vidal, J. L. (2011). Fast attainment of computer cursor control with noninvasively acquired brain signals. J. Neural Eng., 8(3), 036010.
5. Cajigas, I., & Srinivasan, L. (2012). Erratum: "A State-Space Analysis for Reconstruction of Goal-Directed Movements Using Neural Signals." Neural Computation, 24(4), 1106-1107.
6. Cunningham, J. P., Nuyujukian, P., Gilja, V., Chestek, C. A., Ryu, S. I., & Shenoy, K. V. (2011). A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. J. Neurophysiol., 105(4), 1932-1949.
7. Dangi, S., Gowda, S., Heliot, R., Carmena, J.M. (2011). Adaptive Kalman filtering for closed-loop brain-machine interface systems. In Proceedings of the 5th IEEE EMBS Conference on Neural Engineering (pp. 609-612). Piscataway, NJ: IEEE.
8. DiGiovanna, J., Mahmoudi, B., Fortes, J., Principe, J. C., & Sanchez, J. C. (2009). Coadaptive brain-machine interface via reinforcement learning. IEEE Trans. Biomed. Eng., 56(1), 54-64.
9. Eden, U. T., Frank, L.M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16, 971-998.
10. Gage, G. J., Ludwig, K. A., Otto, K. J., Ionides, E. L., & Kipke, D. R. (2005). Naive coadaptive cortical control. J. Neural Eng., 2(2), 52-63.
11. Ganguly, K., & Carmena, J. M. (2009). Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol, 7(7), e1000153.
12. Ganguly, K., & Carmena, J. M. (2010). Neural correlates of skill acquisitionwith a cortical brain-machine interface. J. Mot. Behav., 42(6), 355-360. doi:10.1080/00222895 .2010.526457
13. Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., et al. (2012). A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci., 15(12), 1752-1757. doi:10.1038/nn.3265
14. Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Ryu, S. I., et al. (2010). A high-performance continuous cortically-controlled prosthesis enabled by feedback control design. SfNAbstractViewer/Itinerary Planner, Program/Poster 20.26.
15. Golub, M. D., Yu, B. M., & Chase, S. M. (2012, Aug. 28 2012-Sept. 1 2012). Internal models engaged by brain-computer interface control. Paper presented at the Engineering in Medicine and Biology Society, 2012 Annual International Conference of the IEEE.
16. Gurel, T., & Mehring, C. (2012). Unsupervised adaptation of brain-machine interface decoders. Front. Neurosci., 6, 164. doi:10.3389/fnins.2012.00164
17. Heliot, R., Ganguly, K., Jimenez, J., & Carmena, J. M. (2010). Learning in closed-loop brain-machine interfaces: Modeling and experimental validation. IEEE Trans. Syst. Man Cybern. B Cybern., 40(5), 1387-1397.
18. Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485(7398), 372-375. doi:10.1038/nature11076
19. Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., et al. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171.
20. Judy, J. (2011). Reliable Central-nervous-system interfaces (RCI): A BAA in the program on Reliable Neural Technology (RE-NET). Defense Advanced Research Projects Agency. https://safe.sysplan.com/renet/files/rci/2_Judy.pdf
21. Kim, S. P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., & Black, M. J. (2008). Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng., 5(4), 455-476. doi:10.1088/1741-2560/5/4/010
22. Kowalski, K. (2012). MATLAB-Kinect interface code.
23. Kowalski, K., & Srinivasan, L. (2012). Simulation of two methods in co-adaptive control for brain-machine interfaces. http://hdl.handle.net/1721.1/70975
24. Krakauer, J. W., & Mazzoni, P. (2011). Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol., 21(4), 636-644. doi:10.1016/j.conb.2011 .06.012
25. Lagang, M., & Srinivasan, L. (2013). Stochastic optimal control as a theory of brain-machine interface operation. Neural Comput., 25(2), 374-417. doi:10.1162/ NECO_a_00394
26. Leuthardt, E. C., Pei, X. M., Breshears, J., Gaona, C., Sharma, M., Freudenberg, Z., et al. (2012). Temporal evolution of gamma activity in human cortex during an overt and covert word repetition task. Front. Hum. Neurosci., 6, 99. doi:10.3389/fnhum.2012.00099
27. Li, Z., O'Doherty, J. E., Lebedev, M. A., & Nicolelis, M. A. (2011). Adaptive decoding for brain-machine interfaces through Bayesian parameter updates. Neural Comput., 23(12), 3162-3204. doi:10.1162/NECO_a_00207
28. Mahmoudi, B., & Sanchez, J. C. (2011). A symbiotic brain-machine interface through value-based decision making. PLoS One, 6(3), e14760. doi:10.1371/journal.pone.0014760
29. Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009). Review of control strategies for robotic movement training after neurologic injury. J. Neuroeng. Rehabil., 6, 20. doi:10.1186/1743-0003-6-20
30. McFarland, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2010). Electroencephalographic (EEG) control of three-dimensional movement. J. Neural Eng., 7(3), 036007. doi:10.1088/1741-2560/7/3/036007
31. Miller, K. J., Schalk, G., Fetz, E. E., den Nijs, M., Ojemann, J. G., & Rao, R. P. (2010). Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc. Natl. Acad. Sci. USA, 107(9), 4430-4435. doi:10.1073/pnas .0913697107
32. Moran, D. W., & Schwartz, A. B. (1999). Motor cortical representation of speed and direction during reaching. J. Neurophysiol., 82(5), 2676-2692.
33. Orsborn, A. L., Dangi, S., Moorman, H. G., & Carmena, J. M. (2011). Exploring timescales of closed-loop decoder adaptation in brain-machine interfaces. In Conf. Proc. IEEE Eng. Med. Biol. Soc., 2011 (pp. 5436-5439). Piscataway, NJ: IEEE. doi:10.1109/IEMBS.2011.6091387
34. Orsborn, A. L., Dangi, S., Moorman, H. G., & Carmena, J. M. (2012). Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20, 468-477.
35. Richardson, A. G., Borghi, T., & Bizzi, E. (2012). Activity of the same motor cortex neurons during repeated experience with perturbed movement dynamics. J. Neurophysiol., 107, 3144-3154. doi:10.1152/jn.00477.2011
36. Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., & Shenoy, K. V. (2006). A highperformance brain-computer interface. Nature, 442(7099), 195-198.
37. Schalk, G., Miller, K. J., Anderson, N. R., Wilson, J. A., Smyth, M. D., Ojemann, J. G., et al. (2008). Two-dimensionalmovement control using electrocorticographic signals in humans. J. Neural Eng., 5(1), 75-84. doi:10.1088/1741-2560/5/1/008
38. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Instant neural control of a movement signal. Nature, 416(6877), 141-142.
39. Shenoy, P., Krauledat, M., Blankertz, B., Rao, R. P., & Muller, K. R. (2006). Towards adaptive classification for BCI. J. Neural Eng., 3(1), R13-R23. doi:10.1088/1741-2560/3/1/R02
40. Smith, A. C., Frank, L. M., Wirth, S., Yanike, M., Hu, D., Kubota, Y., et al. (2004). Dynamic analysis of learning in behavioral experiments. J. Neurosci., 24(2), 447-461. doi:10.1523/JNEUROSCI.2908-03.2004
41. Srinivasan, L., Eden, U. T., Mitter, S. K., & Brown, E. N. (2007). General purpose filter design for neural prosthetic devices. J. Neurophysiol., 98, 2456-2475.
42. Srinivasan, L., Eden, U. T., Willsky, A. S., & Brown, E. N. (2005). Goal-directed state equation for tracking reaching movements using neural signals. In Proc. 2nd Internat IEEE EMBS Conf. on Neural Engineering (pp. 352-355). Piscataway, NJ: IEEE.
43. Srinivasan, L., Eden, U. T., Willsky, A. S., & Brown, E. N. (2006). A state-space analysis for reconstruction of goal-directed movements using neural signals. Neural Computation, 18, 465-494.
44. Stengel, R. F. (1994). Optimal control and estimation. New York: Dover.
45. Taylor, D. M., Tillery, S. I., & Schwartz, A. B. (2002). Direct cortical control of 3D neuroprosthetic devices. Science, 296(5574), 1829-1832.
46. Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., & Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol., 93(2), 1074-1089. doi:10.1152/jn.00697.2004
47. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198), 1098-1101. doi:10.1038/nature06996
48. Wolpaw, J. R., McFarland, D. J., Neat, G.W., & Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol., 78(3), 252-259.
49. Yu, B.M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S. I., Meng, T. H., et al. (2007). Mixture of trajectory models for neural decoding of goal-directed movements. J. Neurophysiol., 97, 3763-3780.
50. Zhang, Y., Schwartz, A. B., Chase, S. M., & Kass, R. E. (2012). Bayesian learning in assisted brain-computer interface tasks. In Proceedings of the 34th Annual International Conference of the IEEE EMBS (p. 2740). Piscataway, NJ: IEEE.