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BioMed Research International
Volume 2014 (2014), Article ID 176857, 8 pages
http://dx.doi.org/10.1155/2014/176857
Research Article

A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography

1MEG Center, Department of Neurosurgery, Seoul National University Hospital, Seoul 110-744, Republic of Korea
2Interdisciplinary Program in Neuroscience, Seoul National University College of Natural Sciences, Seoul 151-742, Republic of Korea
3The Planet SK Co., Ltd., Seongnam 463-400, Republic of Korea
4School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
5Department of Neurosurgery, Seoul National University College of Medicine, Seoul 110-744, Republic of Korea
6Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 151-742, Republic of Korea
7Sensory Organ Research Institute, Seoul National University, Seoul 151-742, Republic of Korea

Received 28 March 2014; Accepted 21 May 2014; Published 22 June 2014

Academic Editor: Yiwen Wang

Copyright © 2014 Hong Gi Yeom 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. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. J. P. Donoghue, “Connecting cortex to machines: recent advances in brain interfaces,” Nature Neuroscience, vol. 5, pp. 1085–1088, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. A. B. Schwartz, “Cortical neural prosthetics,” Annual Review of Neuroscience, vol. 27, pp. 487–507, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. 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
  5. L. R. Hochberg, D. Bacher, B. Jarosiewicz et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. J. L. Collinger, B. Wodlinger, J. E. Downey et al., “High-performance neuroprosthetic control by an individual with tetraplegia,” The Lancet, vol. 381, no. 9866, pp. 557–564, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. J. D. Simeral, S.-P. Kim, M. J. Black, J. P. Donoghue, and L. R. Hochberg, “Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array,” Journal of Neural Engineering, vol. 8, no. 2, Article ID 025027, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S.-P. Kim, J. D. Simeral, L. R. Hochberg, J. P. Donoghue, G. M. Friehs, and M. J. Black, “Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 2, pp. 193–203, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Yanagisawa, M. Hirata, Y. Saitoh et al., “Electrocorticographic control of a prosthetic arm in paralyzed patients,” Annals of Neurology, vol. 71, no. 3, pp. 353–361, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Wang, J. L. Collinger, A. D. Degenhart et al., “An electrocorticographic brain interface in an individual with tetraplegia,” PLoS ONE, vol. 8, no. 2, Article ID e55344, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Sakurada, T. Kawase, K. Takano, T. Komatsu, and K. Kansaku, “A BMI-based occupational therapy assist suit: asynchronous control by SSVEP,” Frontiers in Neuroscience, vol. 7, p. 172, 2013. View at Publisher · View at Google Scholar
  12. E. Buch, C. Weber, L. G. Cohen et al., “Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke,” Stroke, vol. 39, no. 3, pp. 910–917, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. K. K. Ang and C. Guan, “Brain-computer interface in stroke rehabilitation,” Journal of Computing Science and Engineering, vol. 7, no. 2, pp. 139–146, 2013. View at Publisher · View at Google Scholar
  14. M. Gomez-Rodriguez, J. Peters, J. Hill, B. Schölkopf, A. Gharabaghi, and M. Grosse-Wentrup, “Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery,” Journal of Neural Engineering, vol. 8, no. 3, Article ID 036005, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Paninski, M. R. Fellows, N. G. Hatsopoulos, and J. P. Donoghue, “Spatiotemporal tuning of motor cortical neurons for hand position and velocity,” Journal of Neurophysiology, vol. 91, no. 1, pp. 515–532, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Wang, S. S. Chan, D. A. Heldman, and D. W. Moran, “Motor cortical representation of position and velocity during reaching,” Journal of Neurophysiology, vol. 97, no. 6, pp. 4258–4270, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. E. Stark, R. Drori, I. Asher, Y. Ben-Shaul, and M. Abeles, “Distinct movement parameters are represented by different neurons in the motor cortex,” European Journal of Neuroscience, vol. 26, no. 4, pp. 1055–1066, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Saleh, K. Takahashi, and N. G. Hatsopoulos, “Encoding of coordinated reach and grasp trajectories in primary motor cortex,” Journal of Neuroscience, vol. 32, no. 4, pp. 1220–1232, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Tankus, Y. Yeshurun, T. Flash, and I. Fried, “Encoding of speed and direction of movement in the human supplementary motor area,” Journal of Neurosurgery, vol. 110, no. 6, pp. 1304–1316, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. C. E. Vargas-Irwin, G. Shakhnarovich, P. Yadollahpour, J. M. K. Mislow, M. J. Black, and J. P. Donoghue, “Decoding complete reach and grasp actions from local primary motor cortex populations,” Journal of Neuroscience, vol. 30, no. 29, pp. 9659–9669, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. T. J. Bradberry, R. J. Gentili, and J. L. Contreras-Vidal, “Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals,” Journal of Neuroscience, vol. 30, no. 9, pp. 3432–3437, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Presacco, R. Goodman, L. Forrester, and J. L. Contreras-Vidal, “Neural decoding of treadmill walking from noninvasive electroencephalographic signals,” Journal of Neurophysiology, vol. 106, no. 4, pp. 1875–1887, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. J. M. Antelis, L. Montesano, A. Ramos-Murguialday, N. Birbaumer, and J. Minguez, “On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals,” PLoS ONE, vol. 8, no. 4, Article ID e61976, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Toda, H. Imamizu, M. Kawato, and M.-A. Sato, “Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods,” NeuroImage, vol. 54, no. 2, pp. 892–905, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Waldert, H. Preissl, E. Demandt et al., “Hand movement direction decoded from MEG and EEG,” Journal of Neuroscience, vol. 28, no. 4, pp. 1000–1008, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Waldert, L. Tüshaus, C. P. Kaller, A. Aertsen, and C. Mehring, “fNIRS exhibits weak tuning to hand movement direction,” PLoS ONE, vol. 7, no. 11, Article ID e49266, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. H. G. Yeom, J. S. Kim, and C. K. Chung, “Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals,” Journal of Neural Engineering, vol. 10, no. 2, Article ID 026006, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Koyama, S. M. Chase, A. S. Whitford, M. Velliste, A. B. Schwartz, and R. E. Kass, “Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control,” Journal of Computational Neuroscience, vol. 29, no. 1-2, pp. 73–87, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Serruya, N. Hatsopoulos, M. Fellows, L. Paninski, and J. Donoghue, “Robustness of neuroprosthetic decoding algorithms,” Biological Cybernetics, vol. 88, no. 3, pp. 219–228, 2003. View at Publisher · View at Google Scholar · View at Scopus
  30. N. G. Hatsopoulos and J. P. Donoghue, “The science of neural interface systems,” Annual Review of Neuroscience, vol. 32, pp. 249–266, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. M. L. Homer, A. V. Nurmikko, J. P. Donoghue, and L. R. Hochberg, “Sensors and decoding for intracortical brain computer interfaces,” Annual Review of Biomedical Engineering, vol. 15, pp. 383–405, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. R. Quian Quiroga and S. Panzeri, “Extracting information from neuronal populations: information theory and decoding approaches,” Nature Reviews Neuroscience, vol. 10, no. 3, pp. 173–185, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. M. M. Churchland, B. M. Yu, M. Sahani, and K. V. Shenoy, “Techniques for extracting single-trial activity patterns from large-scale neural recordings,” Current Opinion in Neurobiology, vol. 17, no. 5, pp. 609–618, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. 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
  35. 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
  36. B. M. Yu, C. Kemere, G. Santhanam et al., “Mixture of trajectory models for neural decoding of goal-directed movements,” Journal of Neurophysiology, vol. 97, no. 5, pp. 3763–3780, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Kemere, K. V. Shenoy, and T. H. Meng, “Model-based neural decoding of reaching movements: a maximum likelihood approach,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 925–932, 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. 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
  39. L. Shpigelman, Y. Singer, R. Paz, and E. Vaadia, “Spikernels: predicting arm movements by embedding population spike rate patterns in inner-product spaces,” Neural Computation, vol. 17, no. 3, pp. 671–690, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. S. G. Mason, A. Bashashati, M. Fatourechi, K. F. Navarro, and G. E. Birch, “A comprehensive survey of brain interface technology designs,” Annals of Biomedical Engineering, vol. 35, no. 2, pp. 137–169, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. J. Ashe and A. P. Georgopoulos, “Movement parameters and neural activity in motor cortex and area 5,” Cerebral Cortex, vol. 4, no. 6, pp. 590–600, 1994. View at Google Scholar · View at Scopus
  42. K. Jerbi, J.-P. Lachaux, K. N'Diaye et al., “Coherent neural representation of hand speed in humans revealed by MEG imaging,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 18, pp. 7676–7681, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Darmanjian, S. P. Kim, M. C. Nechyba et al., “Bimodal brain-machine interface for motor control of robotic prosthetic,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3612–3617, Las Vegas, Nev, USA, October 2003. View at Publisher · View at Google Scholar · View at Scopus
  44. I. S. MacKenzie, T. Kauppinen, and M. Silfverberg, “Accuracy measures for evaluating computer pointing devices,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 9–16, Seattle, Wash, USA, March-April 2001. View at Publisher · View at Google Scholar · View at Scopus
  45. E. A. Felton, R. G. Radwin, J. A. Wilson, and J. C. Williams, “Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals,” Journal of Neural Engineering, vol. 6, no. 5, Article ID 056002, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. M. Hämäläinen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain,” Reviews of Modern Physics, vol. 65, no. 2, pp. 413–497, 1993. View at Google Scholar · View at Scopus
  47. S. Taulu and J. Simola, “Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements,” Physics in Medicine and Biology, vol. 51, no. 7, pp. 1759–1768, 2006. View at Publisher · View at Google Scholar · View at Scopus