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Chinese Science Bulletin

, Volume 57, Issue 16, pp 2036–2045 | Cite as

Development of an invasive brain-machine interface with a monkey model

  • QiaoSheng Zhang
  • ShaoMin Zhang
  • YaoYao Hao
  • HuaiJian Zhang
  • JunMing Zhu
  • Ting Zhao
  • JianMin Zhang
  • YiWen Wang
  • XiaoXiang Zheng
  • WeiDong ChenEmail author
Open Access
Article Cybernetics

Abstact

Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey’s arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.

Keywords

brain-machine interface primary motor cortex center-out paradigm neural decoding support vector machine general regression neural network 

References

  1. 1.
    Gilja V, Chestek C A, Diester I, et al. Challenges and opportunities for next-generation intracortically based neural prostheses. IEEE Trans Biomed Eng, 2011, 58: 1891–1899CrossRefGoogle Scholar
  2. 2.
    Vaadia E, Birbaumer N. Grand challenges of brain computer interfaces in the years to come. Front Neurosci, 2009, 3: 151–154CrossRefGoogle Scholar
  3. 3.
    Hatsopoulos N G, Donoghue J P. The science of neural interface systems. Annu Rev Neurosci, 2009, 32: 249–266CrossRefGoogle Scholar
  4. 4.
    Schwartz A B, Cui X T, Weber D J, et al. Brain-controlled interfaces: Movement restoration with neural prosthetics. Neuron, 2006, 52: 205–220CrossRefGoogle Scholar
  5. 5.
    Lebedev M A, Nicolelis M A. Brain-machine interfaces: Past, present and future. Trends Neurosci, 2006, 29: 536–546CrossRefGoogle Scholar
  6. 6.
    Nicolelis M A. Actions from thoughts. Nature, 2001, 409: 403–407CrossRefGoogle Scholar
  7. 7.
    Donoghue J P. Bridging the brain to the world: A perspective on neural interface systems. Neuron, 2008, 60: 511–521CrossRefGoogle Scholar
  8. 8.
    Chapin J K, Moxon K A, Markowitz R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci, 1999, 2: 664–670CrossRefGoogle Scholar
  9. 9.
    Hochberg L R, Donoghue J P. Sensors for brain-computer interfaces. IEEE Eng Med Biol Mag, 2006, 25: 32–38CrossRefGoogle Scholar
  10. 10.
    Watanabe H, Takahashi H, Nakao M, et al. Intravascular neural interface with nanowire electrode. Electron Commun Jpn, 2009, 92: 29–37Google Scholar
  11. 11.
    Shenoy K V, Chestek C A, Gilja V, et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J Neural Eng, 2011, 8: 1–11Google Scholar
  12. 12.
    Stieglitz T, Rubehn B, Henle C, et al. Brain-computer interfaces: An overview of the hardware to record neural signals from the cortex. Prog Brain Res, 2009, 175: 297–315CrossRefGoogle Scholar
  13. 13.
    Quiroga R Q, Panzeri S. Extracting information from neuronal populations: Information theory and decoding approaches. Nat Rev Neurosci, 2009, 10: 173–185CrossRefGoogle Scholar
  14. 14.
    Gupta R, Ashe J. Offline decoding of end-point forces using neural ensembles: Application to a brain-machine interface. IEEE Trans Neural Syst Rehabil Eng, 2009, 17: 254–262CrossRefGoogle Scholar
  15. 15.
    Milan R J, Carmena J M. Invasive or noninvasive: Understanding brain-machine interface technology. IEEE Eng Med Biol Mag, 2010, 29: 16–22CrossRefGoogle Scholar
  16. 16.
    Leeb R, Sagha H, Chavarriaga R, et al. A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. J Neural Eng, 2011, 8: 1–5CrossRefGoogle Scholar
  17. 17.
    Zhao Q B, Zhang L Q, Cichocki A. EEG-based asynchronous BCI control of a car in 3D virtual reality environments. Chin Sci Bull, 2009, 54: 78–87CrossRefGoogle Scholar
  18. 18.
    Long J, Li Y, Yu T, et al. Target selection with hybrid feature for BCI-based 2-D cursor control. IEEE Trans Biomed Eng, 2011, 14: 1–8Google Scholar
  19. 19.
    Hammon P, Makeig S, Poizner H, et al. Predicting reaching targets from human EEG. IEEE Signal Process Mag, 2008, 25: 69–77CrossRefGoogle Scholar
  20. 20.
    Stevenson I H, Cherian A, London B M, et al. Statistical assessment of the stability of neural movement representations. J Neurophysiol, 2011, 106: 764–774CrossRefGoogle Scholar
  21. 21.
    Ince N F, Gupta R, Arica S, et al. High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials. PLoS ONE, 2010, 5: 1–11CrossRefGoogle Scholar
  22. 22.
    Zhang S, Jiang B, Zhu J, et al. A study on combining local field potential and single unit activity for better neural decoding. Int J Imag Syst Tech, 2011, 21: 165–172CrossRefGoogle Scholar
  23. 23.
    Yu Y, Zhang S M, Zhang H J, et al. Neural decoding based on probabilistic neural network. J Zhejiang Univ Sci B, 2010, 11: 298–306CrossRefGoogle Scholar
  24. 24.
    Velliste M, Perel S, Spalding M C, et al. Cortical control of a prosthetic arm for self-feeding. Nature, 2008, 453: 1098–1101CrossRefGoogle Scholar
  25. 25.
    Moritz C T, Perlmutter S I, Fetz E E. Direct control of paralysed muscles by cortical neurons. Nature, 2008, 456: 639–642CrossRefGoogle Scholar
  26. 26.
    Wessberg J, Stambaugh C R, Kralik J D, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 2000, 408: 361–365CrossRefGoogle Scholar
  27. 27.
    Serruya M D, Hatsopoulos N G, Paninski L, et al. Instant neural control of a movement signal. Nature, 2002, 416: 141–142CrossRefGoogle Scholar
  28. 28.
    Simeral J D, Kim S P, Black M J, et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng, 2011, 8: 1–24CrossRefGoogle Scholar
  29. 29.
    Orban G A, Van E D, Vanduffel W. Comparative mapping of higher visual areas in monkeys and humans. Trends Cognit Sci, 2004, 8: 315–324CrossRefGoogle Scholar
  30. 30.
    Carpaneto J, Umiltà M A, Fogassi L, et al. Decoding the activity of grasping neurons recorded from the ventral premotor area F5 of the macaque monkey. Neuroscience, 2011, 188: 80–94CrossRefGoogle Scholar
  31. 31.
    Nicolelis M A, Dimitrov D, Carmena J M, et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc Natl Acad Sci USA, 2003, 100: 11041–11046CrossRefGoogle Scholar
  32. 32.
    Hendrix C M, Mason C R, Ebner T J. Signaling of grasp dimension and grasp force in dorsal premotor cortex and primary motor cortex neurons during reach to grasp in the monkey. J Neurophysiol, 2009, 102: 132–145CrossRefGoogle Scholar
  33. 33.
    Green A M, Kalaska J F. Learning to move machines with the mind. Trends Neurosci, 2011, 34: 61–75CrossRefGoogle Scholar
  34. 34.
    Georgopoulos A P, Schwartz A B, Kettner R E. Neuronal population coding of movement direction. Science, 1986, 233: 1416–1419CrossRefGoogle Scholar
  35. 35.
    Georgopoulos A P, Kalaska J F, Caminiti R, et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci, 1982, 2: 1527–1537Google Scholar
  36. 36.
    Ganguly K, Secundo L, Ranade G, et al. Cortical representation of ipsilateral arm movements in monkey and man. J Neurosci, 2009, 29: 12948–12956CrossRefGoogle Scholar
  37. 37.
    Kemere C, Santhanam G, Yu B M, et al. Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. J Neurophysiol, 2008, 100: 2441–2452CrossRefGoogle Scholar
  38. 38.
    Vargas-Irwin C E, Shakhnarovich G, Yadollahpour P, et al. Decoding complete reach and grasp actions from local primary motor cortex populations. J Neurosci, 2010, 30: 9659–9669CrossRefGoogle Scholar
  39. 39.
    Taylor D M, Helms S I, Schwartz A B. Direct cortical control of 3D neuroprosthetic devices. Science, 2002, 296: 1829–1832CrossRefGoogle Scholar
  40. 40.
    Acharya S, Tenore F, Aggarwal V, et al. Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area. IEEE Trans Neural Syst Rehabil Eng, 2008, 16: 15–23CrossRefGoogle Scholar
  41. 41.
    Olson B P, Si J, Hu J, et al. Closed-loop cortical control of direction using support vector machines. IEEE Trans Neural Syst Rehabil Eng, 2005, 13: 72–80CrossRefGoogle Scholar
  42. 42.
    Stark E, Abeles M. Predicting movement from multiunit activity. J Neurosci, 2007, 27: 8387–8394CrossRefGoogle Scholar
  43. 43.
    Zhang Q, Zhang S, Lin J, et al. Building brain machine interfaces: From rat to monkey. In: Yen J Y, Hsu P L, Tsai S H, eds. Proceedings of 2011 8th Asian Control Conference, 2011 May 15–18, Kaohsiung. California: IEEE Computer Society, 2011. 886–891Google Scholar
  44. 44.
    Suner S, Fellows M R, Vargas-Irwin C, et al. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex. IEEE Trans Neural Syst Rehabil Eng, 2005, 13: 524–541CrossRefGoogle Scholar
  45. 45.
    Meyer D, Leisch F, Hornik K. The support vector machine under test. Neurocomputing, 2003, 55: 169–186CrossRefGoogle Scholar
  46. 46.
    Specht D F. A general regression neural network. IEEE Trans Neural Netw, 1991, 2: 568–576CrossRefGoogle Scholar
  47. 47.
    Parzen E. On estimation of a probability density function and mode. Ann Math Stat, 1962, 33: 1065–1076CrossRefGoogle Scholar
  48. 48.
    Chestek C A, Batista A P, Santhanam G, et al. Single-neuron stability during repeated reaching in macaque premotor cortex. J Neurosci, 2007, 27: 10742–10750CrossRefGoogle Scholar
  49. 49.
    Amirikian B, Georgopulos A P. Directional tuning profiles of motor cortical cells. Neurosci Res, 2000, 36: 73–79CrossRefGoogle Scholar
  50. 50.
    Fitzsimmons N A, Lebedev M A, Peikon I D, et al. Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity. Front Integr Neurosci, 2009, 3: 1–19CrossRefGoogle Scholar
  51. 51.
    Lebedev M A, Carmena J M, O’Doherty J E, et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci, 2005, 25: 4681–4693CrossRefGoogle Scholar
  52. 52.
    Ma W J, Beck J M, Latham P E, et al. Bayesian inference with probabilistic population codes. Nat Neurosci, 2006, 9: 1432–1438CrossRefGoogle Scholar
  53. 53.
    Zhou F, Liu J, Yu Y, et al. Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats. J Neurosci Methods, 2010, 185: 299–306CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • QiaoSheng Zhang
    • 1
    • 3
    • 4
  • ShaoMin Zhang
    • 1
    • 3
    • 4
  • YaoYao Hao
    • 1
    • 3
    • 4
  • HuaiJian Zhang
    • 1
    • 3
    • 4
  • JunMing Zhu
    • 1
    • 5
  • Ting Zhao
    • 1
  • JianMin Zhang
    • 5
  • YiWen Wang
    • 1
  • XiaoXiang Zheng
    • 1
    • 3
    • 4
  • WeiDong Chen
    • 1
    • 2
    Email author
  1. 1.Qiushi Academy for Advanced StudiesZhejiang UniversityHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  3. 3.Department of Biomedical EngineeringZhejiang UniversityHangzhouChina
  4. 4.Key Laboratory of Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouChina
  5. 5.Department of Neurosurgery, Second Affiliated Hospital School of MedicineZhejiang UniversityHangzhouChina

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