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Brain–Machine Interfaces Based on Computational Model

  • Yasuharu Koike
  • Hiroyuki Kambara
  • Natsue Yoshimura
  • Duk Shin

Abstract

The research about brain computer interface or brain machine interface has been widely developed in this decade. Implant methods are already used for eye or ear as retinal implant or cochlear implant, these devices stimulate peripheral nerve. In this case, the stimulus site is peripheral and the information from each sensor is input signal of the brain. Brain Machine Interface measure or stimulate neuron in the brain directly and decode neuronal firings to generate information. It is impossible to measure all neuron activities from brain, because of enormous quantity of neurons and also the function is unknown. So anatomical knowledge, such as a cortical homunculus of the primary motor cortex and the primary somatosensory cortex, or neural scientific knowledge is used.

The process of movement from the primary motor cortex to muscle is forward direction, and the number of neurons are decrease in this process. The generation of muscle activities are straightforward. In the field of motor control, motor command generation is still open problem. There are many theories are proposed. In order to evaluate or verify these theories, the technique of BMI is also useful. In this chapter, we introduce musculo-skeletal model and computational model for movement, and also some examples of BMI/BCI.

Keywords

Joint Angle Supplementary Motor Area Motor Command Primary Motor Cortex Joint Torque 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chapin JK, Moxon KA, Markowitz RS, Nicolelis MAL (1999) Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 2:664–670PubMedCrossRefGoogle Scholar
  2. 2.
    Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MAL (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1(2):1–16CrossRefGoogle Scholar
  3. 3.
    Musallam S, Corneil BD, Greger B, Scherberger H, Andersen RA (2004) Cognitive control signals for neural prosthetics. Science 305(5681):258–262PubMedCrossRefGoogle Scholar
  4. 4.
    Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164–171PubMedCrossRefGoogle Scholar
  5. 5.
    Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT (1982) On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci 2(11):1527–1537PubMedGoogle Scholar
  6. 6.
    Fetz EE, Cheney PD, German DC (1976) Corticomotoneuronal connections of precentral cells detected by postspike averages of emg activity in behaving monkeys. Brain Res 114(3):505–510PubMedCrossRefGoogle Scholar
  7. 7.
    Kalaska JF, Cohen DA, Hyde ML, Prud’homme M (1989) A comparison of movement ­direction-related versus load direction-related activity in primate motor cortex, using a ­two-dimensional reaching task. J Neurosci 9(6):2080–2102PubMedGoogle Scholar
  8. 8.
    Koike Y, Kawato M (1994) Estimation of arm posture in {3D}-space from surface EMG ­signals using a neural network model. IEICE Trans Fundam E77-D, No. 4:368–375Google Scholar
  9. 9.
    Koike Y, Kawato M (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biol Cybern 73:291–300PubMedCrossRefGoogle Scholar
  10. 10.
    Kim J, Sato M, Koike Y (2002) Human arm posture control using the impedance controllability of the musculo-skeletal system against the alteration of the environments. Trans Control Autom Syst Eng 4(1):43–48Google Scholar
  11. 11.
    Feldman AG, Adamovich SV, Ostry DJ, Flanagan JR (1990) The origin of electromyograms – explanations based on the equilibrium point hypotheses. In: Winters JM, Woo SL-Y (eds) Multiple muscle systems. Springer, New York, pp 195–213CrossRefGoogle Scholar
  12. 12.
    Flanagan JR, Ostry DJ, Feldman AG (1993) Control of trajectory modifications in target-directed reaching. J Mot Behav 25(3):140–152PubMedCrossRefGoogle Scholar
  13. 13.
    Katayama M, Kawato M (1993) Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse models. Biol Cybern 69(5/6):353–362PubMedGoogle Scholar
  14. 14.
    Gribble PL, Ostry DJ, Sanguineti V, Laboissiere R (1998) Are complex control signals required for human arm movement? J Neurophysiol 79(3):1409–1424PubMedGoogle Scholar
  15. 15.
    Osu R, Gomi H (1999) Multijoint muscle regulation mechanisms examined by measured human arm stiffness and emg signals. J Neurophysiol 81:1458–1468PubMedGoogle Scholar
  16. 16.
    Prilutsky BI (2000) Coordination of two- and one-joint muscles: functional consequences and implications for motor control. Mot Control 4(1):1–44Google Scholar
  17. 17.
    Ghez C (2000) Principles of neural science, chapter Muscles: effectors of the motor systems. McGraw-Hill, New YorkGoogle Scholar
  18. 18.
    Özkaya N, Nordin M (1991) Fundamentals of biomechanics: equilibrium, motion, and deformation. Van Nostrand Reinhold, New YorkGoogle Scholar
  19. 19.
    Kawato M, Gomi H (1993) The cerebellum and VOR/OKR learning models. Trends Neurosci 16(11):177–178CrossRefGoogle Scholar
  20. 20.
    Inman VT, Ralston HJ, Saunders JB, Feinstein B, Wright EW Jr (1952) Relation of human electromyogram to muscular tension. Electroencephalogr Clin Neurophysiol 4(2):187–194PubMedCrossRefGoogle Scholar
  21. 21.
    Gottlieb GL, Agarwal GC (1971) Dynamic relationship between isometric muscle tension and the electromyogram in man. J Appl Physiol 30(3):345–351PubMedGoogle Scholar
  22. 22.
    Basmajian JV, De Luca CJ (1985) Description and analysis of the EMG signal. Williams & Wilkins, Baltimore, MDGoogle Scholar
  23. 23.
    Maton B, Peres G, Landjerit B (1987) Relationships between individual isometric muscle forces, emg activity and joint torque in monkeys. Eur J Appl Physiol Occup Physiol 56(4):487–494PubMedCrossRefGoogle Scholar
  24. 24.
    Clancy EA, Hogan N (1991) Estimation of joint torque from the surface EMG. Annu Int Conf IEEE Eng Med Biol Soc 13(2):0877–0878Google Scholar
  25. 25.
    Choi K, Hirose H, Sakurai Y, Iijima T, Koike Y (2009) Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture. Neural Netw 22(9):1214–1223PubMedCrossRefGoogle Scholar
  26. 26.
    Jacobs RA, Jordan MI (1991) A competitive modular connectionist architecture. In: Moody JM, Hanson SJ, Lippmann RP (eds) Advances in neural information processing systems 3. Morgan Kaufmann, San Meteo, pp 767–773Google Scholar
  27. 27.
    Kawato M (1999) Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9(6):718–727PubMedCrossRefGoogle Scholar
  28. 28.
    Cohen YE, Andersen RA (2002) A common reference frame for movement plans in the posterior parietal cortex. Nat Rev Neurosci 3(7):553–562PubMedCrossRefGoogle Scholar
  29. 29.
    Matsumura M, Kubota K (1979) Cortical projection of hand-arm motor area from post-arcuate area in macaque monkey: a histological study of retrograde transport of horseradish peroxidase. Neurosci Lett 11:241–246PubMedCrossRefGoogle Scholar
  30. 30.
    Muakkassa KF, Strick PL (1979) Frontal lobe inputs to primate motor cortex: evidence for four somatotopically organized premotor areas. Brain Res 177:176–182PubMedCrossRefGoogle Scholar
  31. 31.
    Kakei S, Hoffman DS, Strick PL (1999) Muscle and movement representations in the primary motor cortex. Science 285(5436):2136–2139PubMedCrossRefGoogle Scholar
  32. 32.
    Kakei S, Hoffman DS, Strick PL (2001) Direction of action is represented in the ventral premotor cortex. Nat Neurosci 4(10):1020–1025PubMedCrossRefGoogle Scholar
  33. 33.
    Fried I, Katz A, McCarthy G, Sass KJ, Williamson P, Spencer SS, Spencer DD (1991) Functional organization of human supplementary motor cortex studied by electrical stimulation. J Neurosci 11:3656–3666PubMedGoogle Scholar
  34. 34.
    Shima K, Tanji J (1994) Role for supplementary motor area cells in planning several movements ahead. Nature 371:413–416PubMedCrossRefGoogle Scholar
  35. 35.
    Roland PE, Larsen B, Lassen NA, Skinhoj E (1980) Supplementary motor area and other cortical areas in organization of voluntary movements in man. J Neurophysiol 43(1):118–136PubMedGoogle Scholar
  36. 36.
    Halsband U, Matsuzaka Y, Tanji J (1994) Neuronal activity in the primate supplementary, pre-supplementary and premotor cortex during externally and internally instructed sequential movements. Neurosci Res 20(2):149–155PubMedCrossRefGoogle Scholar

Copyright information

© Springer 2011

Authors and Affiliations

  • Yasuharu Koike
    • 1
    • 2
  • Hiroyuki Kambara
    • 1
  • Natsue Yoshimura
    • 1
  • Duk Shin
    • 1
  1. 1.Tokyo Institute of TechnologyYokohamaJapan
  2. 2.JST CRESTKawaguchiJapan

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