Advertisement

Brain–Computer Interfaces

  • Bin He
  • Shangkai Gao
  • Han Yuan
  • Jonathan R. Wolpaw
Chapter

Abstract

Brain–computer interfaces are a new technology that could help to restore useful function to people severely disabled by a wide variety of devastating neuromuscular disorders and to enhance functions in healthy individuals. The first demonstrations of brain–computer interface (BCI) technology occurred in the 1960s when Grey Walter used the scalp-recorded electroencephalogram (EEG) to control a slide projector in 1964 [1] and when Eberhard Fetz taught monkeys to control a meter needle (and thereby earn food rewards) by changing the firing rate of a single cortical neuron [2, 3]. In the 1970s, Jacques Vidal developed a system that used the scalp-recorded visual evoked potential (VEP) over the visual cortex to determine the eye-gaze direction (i.e., the visual fixation point) in humans, and thus to determine the direction in which a person wanted to move a computer cursor [4, 5]. At that time, Vidal coined the term “brain–computer interface.” Since then and into the early 1990s, BCI research studies continued to appear only every few years. In 1980, Elbert et al. showed that people could learn to control slow cortical potentials (SCPs) in scalp-recorded EEG activity and could use that control to adjust the vertical position of a rocket image moving across a TV screen [6]. In 1988, Farwell and Donchin [7] reported that people could use scalp-recorded P300 event-related potentials (ERPs) to spell words on a computer screen. Wolpaw and his colleagues trained people to control the amplitude of mu and beta rhythms (i.e., sensorimotor rhythms) in the EEG recorded over the sensorimotor cortex and showed that the subjects could use this control to move a computer cursor rapidly and accurately in one or two dimensions [8, 9].

Keywords

Motor Imagery Visual Evoke Potential Slow Cortical Potential Information Transfer Rate Sensorimotor Rhythm 
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.

Notes

Acknowledgment

This work was supported in part by NSF CBET-0933067, DGE-1069104, NIH EB007920, and EB006433, by NSF of China-90820304, as well as by NIH HD30146 and EB00856.

References

  1. 1.
    Graimann B, Allison B, Pfurtscheller G (2010) Brain-computer interfaces: a gentle introduction. In: Graimann B, Allison B, Pfurtscheller G (eds) Brain-computer interfaces. Springer, Berlin, pp 1–27CrossRefGoogle Scholar
  2. 2.
    Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163:955–958CrossRefGoogle Scholar
  3. 3.
    Fetz EE, Finocchio DV (1971) Operant conditioning of specific patterns of neural and muscular activity. Science 174:431–435CrossRefGoogle Scholar
  4. 4.
    Vidal JJ (1973) Towards direct brain–computer communication. Annu Rev Biophys Bioeng 2:157–180CrossRefGoogle Scholar
  5. 5.
    Vidal JJ (1977) Real-time detection of brain events in EEG. IEEE Proc 65:633–664CrossRefGoogle Scholar
  6. 6.
    Elbert T, Rockstroh B, Lutzenberger W, Birbaumer N (1980) Biofeedback of slow cortical potentials. I. Electroencephalogr Clin Neurophysiol 48:293–301CrossRefGoogle Scholar
  7. 7.
    Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523CrossRefGoogle Scholar
  8. 8.
    Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78:252–259CrossRefGoogle Scholar
  9. 9.
    Wolpaw JR, McFarland DJ (1994) Multichannel EEG-based brain-computer communication. Electroencephalogr Clin Neurophysiol 90:444–449CrossRefGoogle Scholar
  10. 10.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791CrossRefGoogle Scholar
  11. 11.
    Vallabhaneni A, Wang T, He B (2005) Brain computer interface. In: He B (ed) Neural engineering. Kluwer Academic, Plenum, New York, pp 85–122Google Scholar
  12. 12.
    Wolpaw JR, Wolpaw EW (eds) (2012) Brain-computer interfaces: principles and practice. Oxford University Press, OxfordGoogle Scholar
  13. 13.
    Wolpaw JR, Wolpaw EW (2012) Brain-computer interfaces: something new under the sun. In: Wolpaw JR, Wolpaw EW (eds) Brain-computer interfaces: principles and practice. Oxford University Press, Oxford, pp 3–12CrossRefGoogle Scholar
  14. 14.
    Sutter EE (1992) The brain response interface: communication through visually-induced electrical brain responses. J Microcomput Appl 15:31–45CrossRefGoogle Scholar
  15. 15.
    Graimann B, Allison B, Pfurtscheller G (eds) (2010b) Brain-computer interfaces. Springer, Berlin, p 21 et passimGoogle Scholar
  16. 16.
    McCrea DA, Ryback IA (2008) Organization of mammalian locomotor rhythm and pattern generation. Brain Res Rev 57:134–146CrossRefGoogle Scholar
  17. 17.
    Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653CrossRefGoogle Scholar
  18. 18.
    Guertin PA, Steuer I (2009) Key central pattern generators of the spinal cord. J Neurosci Res 87:2399–2405CrossRefGoogle Scholar
  19. 19.
    Carroll RC, Zukin RS (2002) NMDA-receptor trafficking and targeting: implications for synaptic transmission and plasticity. Trends Neurosci 25(11):571–577CrossRefGoogle Scholar
  20. 20.
    Gaiarsa JL, Caillard O, Ben-Ari Y (2002) Long-term plasticity at GABA-ergic and glycinergic synapses: mechanisms and functional significance. Trends Neurosci 25(11):564–570CrossRefGoogle Scholar
  21. 21.
    Vaynman S, Gomez-Pinilla F (2005) License to run: exercise impacts functional plasticity in the intact and injured central nervous system by using neurotrophins. Neurorehabil Neural Repair 19(4):283–295CrossRefGoogle Scholar
  22. 22.
    Saneyoshi T, Fortin DA, Soderling TR (2010) Regulation of spine and synapse formation by activity-dependent intracellular signaling pathways. Curr Opin Neurobiol 20(1):108–115CrossRefGoogle Scholar
  23. 23.
    Wolpaw JR (2010) What can the spinal cord teach us about learning and memory? Neuroscientist 16(5):532–549CrossRefGoogle Scholar
  24. 24.
    Yuan H, Liu T, Szarkowski R, Rios C, Ashe J, He B (2010) Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements. Neuroimage 49:2596–2606CrossRefGoogle Scholar
  25. 25.
    Yuan H, Perdoni C, He B (2010) Relationship between speed and EEG activity during imagined and executed hand movements. J Neural Eng 7:26001CrossRefGoogle Scholar
  26. 26.
    Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W, Goebel R, Birbaumer N (2003) Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19(3):577–586CrossRefGoogle Scholar
  27. 27.
    Kipke DR, Shain W, Buzsáki G, Fetz E, Henderson JM, Hetke JF, Schalk G (2008) Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. J Neurosci 28(46):11830–8CrossRefGoogle Scholar
  28. 28.
    Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233:1416–1419CrossRefGoogle Scholar
  29. 29.
    Kennedy PR (1989) The cone electrode: a long-term electrode that records from neurites grown onto its recording surface. J Neurosci Methods 29:181–193CrossRefGoogle Scholar
  30. 30.
    Donoghue JP, Sanes JN (1994) Motor areas of the cerebral cortex. J Clin Neurophysiol 11:382–396Google Scholar
  31. 31.
    Taylor D, Tillery S, Schwartz A (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296:1829–1832CrossRefGoogle Scholar
  32. 32.
    Nicolelis MA, Chapin JK (2002) Controlling robots with the mind. Sci Am 287:46–53CrossRefGoogle Scholar
  33. 33.
    Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB (2008) Cortical control of a prosthetic arm for self-feeding. Nature 453:1098–1101CrossRefGoogle Scholar
  34. 34.
    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 442:164–171CrossRefGoogle Scholar
  35. 35.
    Truccolo W, Friehs GM, Donoghue JP, Hochberg LR (2008) Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. J Neurosci 28:1163–1178CrossRefGoogle Scholar
  36. 36.
    Schwartz AB, Cui XT, Weber DJ, Moran DW (2006) Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52(1):205–20CrossRefGoogle Scholar
  37. 37.
    Reina GA, Moran DW, Schwartz AB (2001) On the relationship between joint angular velocity and motor cortical discharge during reaching. J Neurophysiol 85(6):2576–89Google Scholar
  38. 38.
    Wang W, Chan SS, Heldman DA, Moran DW (2010) Motor cortical representation of hand translation and rotation during reaching. J Neurosci 30:958–962CrossRefGoogle Scholar
  39. 39.
    Shin HC, Aggawal V, Acharya S, Schieber MH, Thakor NV (2010) Neural decoding of finger movements using Skellam based maximum likelihood decoding. IEEE Trans Biomed Eng 57:754–760CrossRefGoogle Scholar
  40. 40.
    Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB (2008) Functional network reorganization during learning in a brain-computer interface paradigm. Proc Natl Acad Sci USA 105(49):19486–91CrossRefGoogle Scholar
  41. 41.
    Simeral JD, Kim SP, Black MJ, Donoghue JP, Hochberg LR (2011) Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng 8(2):025027CrossRefGoogle Scholar
  42. 42.
    He B, Yang L, Wilke C, Yuan H (2011) Electrophysiological imaging of brain activity and connectivity-challenges and opportunities. IEEE Trans Biomed Eng 58(7):1918–31CrossRefGoogle Scholar
  43. 43.
    Manning JR, Jacobs J, Fried I, Kahana MJ (2009) Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J Neurosci 29(43):13613–20CrossRefGoogle Scholar
  44. 44.
    Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW (2004) A brain–computer interface using electrocorticographic signals in humans. J Neural Eng 1:63–71CrossRefGoogle Scholar
  45. 45.
    Schalk G et al (2007) Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 4:264–275CrossRefGoogle Scholar
  46. 46.
    Schalk G et al (2008) Two-dimensional movement control using electrocorticographic signals in humans. J Neural Eng 5:75–84CrossRefGoogle Scholar
  47. 47.
    Leuthardt EC, Gaona C, Sharma M, Szrama N, Roland J, Freudenberg Z, Solis J, Breshears J, Schalk G (2011) Using the electrocorticographic speech network to control a brain-computer interface in humans. J Neural Eng 8(3):036004CrossRefGoogle Scholar
  48. 48.
    Zhang P, Jamison K, Engel S, He B, He S (2011) Binocular rivalry requires visual attention. Neuron 71:362–369CrossRefGoogle Scholar
  49. 49.
    Michel C, He B (2011) EEG mapping and source imaging. In: Schomer D, Lopes da Silva F (eds) Niedermeyer’s electroencephalography, Chap 55, 6th edn. Wolters Kluwer & Lippincott Williams & Wilkins, Philadelphia, pp 1179–1202Google Scholar
  50. 50.
    Malmivuo J, Plonsey R (1995) Bioelectromagnetism - principles and applications of bioelectric and biomagnetic fields. Oxford University Press, New YorkCrossRefGoogle Scholar
  51. 51.
    Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 101:17849–17854CrossRefGoogle Scholar
  52. 52.
    Doud AJ, Lucas JP, Pisansky MT, He B (2011) Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS One 6(10):e26322. doi: 10.1371/journal.pone.0026322 CrossRefGoogle Scholar
  53. 53.
    McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7:036007CrossRefGoogle Scholar
  54. 54.
    Royer AS, Doud AJ, Rose ML, He B (2010) EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. IEEE Trans Neural Syst Rehabil Eng 18(6):581–9CrossRefGoogle Scholar
  55. 55.
    He B (ed) (2004) Modeling and imaging of bioelectrical activity: principle and applications. Kluwer Academic, Plenum, New YorkGoogle Scholar
  56. 56.
    Nunez PL, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG. Oxford University Press, OxfordCrossRefGoogle Scholar
  57. 57.
    Bradberry TJ, Gentili RJ, Contreras-Vidal JL (2010) Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals. J Neurosci 30(9):3432–7CrossRefGoogle Scholar
  58. 58.
    Bradberry TJ, Gentili RJ, Contreras-Vidal JL (2011) Fast attainment of computer cursor control with noninvasively acquired brain signals. J Neural Eng 8(3):036010CrossRefGoogle Scholar
  59. 59.
    Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, Mehring C (2008) Hand movement direction decoded from MEG and EEG. J Neurosci 28:1000–1008CrossRefGoogle Scholar
  60. 60.
    Moran DW, Schwartz AB (1999) Motor cortical representation of speed and direction during reaching. J Neurophysiol 82:2676–2692Google Scholar
  61. 61.
    Qin L, Ding L, He B (2004) Motor imagery classification by means of source analysis for brain-computer interface applications. J Neural Eng 1:135–141CrossRefGoogle Scholar
  62. 62.
    Kamousi B, Liu Z, He B (2005) Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Trans Neural Syst Rehabil Eng 13:166–171CrossRefGoogle Scholar
  63. 63.
    Kamousi B, Amini AN, He B (2007) Classification of motor imagery by means of cortical current density estimation and von neumann entropy. J Neural Eng 4:17–25CrossRefGoogle Scholar
  64. 64.
    Cincotti F, Mattia D, Aloise F, Bufalari S, Astolfi L, Vico Fallani F, Tocci A, Bianchi L, Marciani MG, Gao S, Millan J, Babiloni F (2008) High-resolution EEG techniques for brain–computer interface applications. J Neurosci Methods 167:31–42CrossRefGoogle Scholar
  65. 65.
    Noirhomme Q, Kitney RI, Macq B (2008) Single-trial EEG source reconstruction for brain–computer interface. IEEE Trans Biomed Eng 55:1592–1601CrossRefGoogle Scholar
  66. 66.
    Yuan H, Doud A, Gururajan A, He B (2008) Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain. IEEE Trans Neural Syst Rehabil Eng 16:425–431CrossRefGoogle Scholar
  67. 67.
    Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A (2007) An MEG-based brain-computer interface (BCI). Neuroimage 36(3):581–93CrossRefGoogle Scholar
  68. 68.
    Van Der Werf J, Jensen O, Fries P, Medendorp WP (2010) Neuronal synchronization in human posterior parietal cortex during reach planning. J Neurosci 30(4):1402–12CrossRefGoogle Scholar
  69. 69.
    Darvas F, Scherer R, Ojemann JG, Rao RP, Miller KJ, Sorensen LB (2010) High gamma mapping using EEG. Neuroimage 49(1):930–8CrossRefGoogle Scholar
  70. 70.
    Hämäläinen MS, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography – theory, instrumetation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497CrossRefGoogle Scholar
  71. 71.
    Battapady H, Lin P, Holroyd T, Hallett M, Chen X, Fei DY, Bai O (2009) Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals. Clin Neurophysiol 120(11):1978–87CrossRefGoogle Scholar
  72. 72.
    Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, Ugurbil K (1992) Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci USA 89(13):5951–5CrossRefGoogle Scholar
  73. 73.
    Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R et al (1992) Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA 89(12):5675–9CrossRefGoogle Scholar
  74. 74.
    Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS (1992) Time course EPI of human brain function during task activation. Magn Reson Med 25(2):390–7CrossRefGoogle Scholar
  75. 75.
    Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 87(24):9868–72CrossRefGoogle Scholar
  76. 76.
    Yuan H, Perdoni C, Yang L, He B (2011) Differential electrophysiological coupling for positive and negative BOLD responses during unilateral hand movements. J Neurosci 31(26):9585–93CrossRefGoogle Scholar
  77. 77.
    Cox RW, Jesmanowicz A, Hyde JS (1995) Real-time functional magnetic resonance imaging. Magn Reson Med 33(2):230–6CrossRefGoogle Scholar
  78. 78.
    Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1847CrossRefGoogle Scholar
  79. 79.
    Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. Proc IEEE 89(7):1123–1134CrossRefGoogle Scholar
  80. 80.
    Pfurtscheller G, Neuper C, Flotzinger D (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103(6):642–651CrossRefGoogle Scholar
  81. 81.
    McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR (2000) Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 12:177–186CrossRefGoogle Scholar
  82. 82.
    Wang T, Deng J, He B (2004) Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clin Neurophysiol 115:2744–2753CrossRefGoogle Scholar
  83. 83.
    Wang T, He B (2004) An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in brain computer interface, J Neural Eng 1(1):1–7CrossRefGoogle Scholar
  84. 84.
    Yamawaki N, Wilke C, Liu Z, He B (2006) An enhanced time-frequency approach for motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 14(2):250–254CrossRefGoogle Scholar
  85. 85.
    Miller KJ, Schalk G, Fetz EE, den Nijs M, Ojemann JG, Rao RP (2010) Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci USA 107:4430–4435CrossRefGoogle Scholar
  86. 86.
    Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398(6725):297–298CrossRefGoogle Scholar
  87. 87.
    Birbaumer N, Kübler A, Ghanayim N, Hinterberger T, Perelmouter J, Kaiser J, Iversen I, Kotchoubey B, Neumann N, Flor H (2000) The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehabil Eng 8(2):190–193CrossRefGoogle Scholar
  88. 88.
    Donchin E, Coles MGH (1988) Is the P300 component a manifestation of context updating? Behav Brain Sci 11:355–425Google Scholar
  89. 89.
    Kubler A, Kotchoubey B, Kaiser J, Wolpaw J, Birbaumer N (2001) Brain-computer communication: unlocking the locked in. Psychol Bull 127(3):358–375CrossRefGoogle Scholar
  90. 90.
    Spencer KM, Dien J, Donchin E (2001) Spatiotemporal analysis of the late ERP responses to deviant stimuli. Psychophysiology 38(2):343–358CrossRefGoogle Scholar
  91. 91.
    Sellers EW, Vaughan TM, Wolpaw JR (2010) A brain-computer interface for long-term independent home use. Amyotroph Lateral Scler 11(5):449–455CrossRefGoogle Scholar
  92. 92.
    Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain-computer interfaces based on steady-state visual evoked response. IEEE Trans Rehabil Eng 8(2):211–214CrossRefGoogle Scholar
  93. 93.
    Ortner R, Allison B, Korisek G, Gaggl H, Pfurtscheller G (2011) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 19(1):1–5CrossRefGoogle Scholar
  94. 94.
    Pan J, Gao X, Duan F, Yan Z, Gao S (2011) Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis. J Neural Eng 8:036027CrossRefGoogle Scholar
  95. 95.
    Kennedy PR, Bakay RA (1998) Restoration of neural output from a paralyzed patient by a direct brain connection. NeuroReport 9:1707–1711CrossRefGoogle Scholar
  96. 96.
    Goncharova II, McFarland DJ, Vaughan TM, Wolpaw JR (2003) EMG contamination of EEG: spectral and topographical characteristics. Clin Neurophysiol 114:1580–1593CrossRefGoogle Scholar
  97. 97.
    McFarland DJ, McCane LM, David SV, Wolpaw JR (1997) Spatial filter selection for EEG-based communication. Electroencephalogr Clin Neurophysiol 103:386–394CrossRefGoogle Scholar
  98. 98.
    Hjorth B (1975) An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol 39(5):526–530CrossRefGoogle Scholar
  99. 99.
    Perrin F, Bertrand O, Pernier J (1987) Scalp current density mapping: value and estimation from potential data. IEEE Trans Biomed Eng 34:283–288CrossRefGoogle Scholar
  100. 100.
    He B, Cohen R (1992) Body surface Laplacian ECG mapping. IEEE Trans Biomed Eng 39(11):1179–1191CrossRefGoogle Scholar
  101. 101.
    Le J, Menon V, Gevins A (1992) Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data. Electroencephalogr Clin Neurophysiol 92:433–441Google Scholar
  102. 102.
    Nunez P, Silberstein R, Cadusch P, Wijesinghe R, Westdorp A, Srinivasan R (1994) A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. Electroencephalogr Clin Neurophysiol 90(1):40–57CrossRefGoogle Scholar
  103. 103.
    Babiloni F, Babiloni C, Carducci F, Fattorini L, Onorati P, Urbano A (1996) Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model. Electroencephalogr Clin Neurophysiol 98(4):363–73CrossRefGoogle Scholar
  104. 104.
    He B (1999) Brain electric source imaging: scalp Laplacian mapping and cortical imaging. Crit Rev Biomed Eng 27:149–188Google Scholar
  105. 105.
    He B, Lain J, Li G (2001) High-resolution EEG: a new realistic geometry spline Laplacian estimation technique. Clin Neurophysiol 112(5):845–852CrossRefGoogle Scholar
  106. 106.
    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 2:1527–1537Google Scholar
  107. 107.
    Kettner RE, Schwartz AB, Georgopoulos AP (1988) Primate motor cortex and free arm movements to visual targets in three-dimensional space. III. positional gradients and population coding of movement direction from various movement origins. J Neurosci 8:2938–2947Google Scholar
  108. 108.
    Fu QG, Flament D, Coltz JD, Ebner TJ (1995) Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. J Neurophysiol 73:836–854Google Scholar
  109. 109.
    Schwartz AB (1994) Direct cortical representation of drawing. Science 265:540–542CrossRefGoogle Scholar
  110. 110.
    Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP (2004) Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J Neurophysiol 91:515–532CrossRefGoogle Scholar
  111. 111.
    Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRefGoogle Scholar
  112. 112.
    Blum AL, Langely P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97:245–271MATHCrossRefGoogle Scholar
  113. 113.
    Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446CrossRefGoogle Scholar
  114. 114.
    Babiloni F, Cincotti F, Bianchi L, Pirri G, Millan J, Mourino J, Salinari S, Marciani MG (2001) Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers. Med Eng Phys 23:323–328CrossRefGoogle Scholar
  115. 115.
    Blankertz B, Curio G, Müller K (2002) Classifying single trial EEG: towards brain computer interfacing. Adv Neural Inf Proc Syst 14:157–164Google Scholar
  116. 116.
    Cincotti F, Mattia D, Babiloni C, Carducci F, Bianchi L, Millan J, Mourino J, Salinari S, Marciani M, Babiloni F (2002) Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance based classifiers. Method Inform Med 41:337–341Google Scholar
  117. 117.
    Peters BO, Pfurtscheller G, Flyvbjerg H (1998) Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface. Neural Netw 11:1429–1433CrossRefGoogle Scholar
  118. 118.
    Robert C, Gaudy J, Limoge A (2002) Electroencephalogram processing using neural networks. Clin Neurophysiol 113:694–701CrossRefGoogle Scholar
  119. 119.
    Deng J, He B (2003) Classification of imaginary tasks from three channels of EEG by using an artificial neural network. In: Proceedings of 25th international conference on IEEE EMBS, CD-ROMGoogle Scholar
  120. 120.
    Vallabhaneni A, He B (2004) Motor imagery task classification for brain computer interface applications using spatio-temporal principle component analysis. Neurol Res 26(3):282–287CrossRefGoogle Scholar
  121. 121.
    Obermaier B, Guger C, Neuper C, Pfurthscheller G (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recogn Lett 22:1299–1309MATHCrossRefGoogle Scholar
  122. 122.
    Curran E, Sykacek P, Stokes M, Roberts SJ, Penny W, Johnsrude I, Owen AM (2004) Cognitive tasks for driving a brain–computer interfacing system: a pilot study. IEEE Trans Neural Syst Rehabil Eng 12:48–54CrossRefGoogle Scholar
  123. 123.
    Lemm S, Schafer C, Curio G (2004) BCI competition 2003–data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements. IEEE Trans Biomed Eng 51:1077–80CrossRefGoogle Scholar
  124. 124.
    Bashashati A, Fatourechi M, Wardand RK, Birch GE (2007) A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (Topical review). J Neural Eng 4:R32–R57. doi: 10.1088/1741-2560/4/2/R03 CrossRefGoogle Scholar
  125. 125.
    Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces (Topical review). J Neural Eng 4:R1–R13. doi: 10.1088/1741-2560/4/2/R01 CrossRefGoogle Scholar
  126. 126.
    Krusienski DJ, McFarland DJ, Principe JC (2012) BCI signal processing: feature extraction. In: Wolpaw JR, Wolpaw EW (eds) Brain-computer interfaces: principles and practice. Oxford University Press, Oxford, pp 123–146Google Scholar
  127. 127.
    McFarland DJ, Krusienski DJ (2012) BCI signal processing: feature translation. In: Wolpaw JR, Wolpaw EW (eds) Brain-computer interfaces: principles and practice. Oxford University Press, Oxford, pp 147–164Google Scholar
  128. 128.
    Moritz CT, Perlmutter SI, Fetz EE (2008) Direct control of paralysed muscles by cortical neurons. Nature 456:639–642CrossRefGoogle Scholar
  129. 129.
    Tam W, Tong K, Meng F, Gao S (2011) A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study. IEEE Trans Neural Syst Rehabil Eng 19(6):617–627CrossRefGoogle Scholar
  130. 130.
    Buch E, Weber C, Cohen LG, Braun C, Dimyan MA, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N (2008) Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39(3):910–7CrossRefGoogle Scholar
  131. 131.
    Dimyan MA, Cohen LG (2011) Neuroplasticity in the context of motor rehabilitation after stroke. Nat Rev Neurol 7(2):76–85CrossRefGoogle Scholar
  132. 132.
    Alon G, Sunnerhagen KS, Geurts AC, Ohry A (2003) A home-based, selfadministered stimulation program to improve selected hand functions of chronic stroke. NeuroRehabilitation 18:215–25Google Scholar
  133. 133.
    Ring H, Rosenthal N (2005) Controlled study of neuroprosthetic functional electrical stimulation in sub-acute post-stroke rehabilitation. J Rehabil Med 37:32–36CrossRefGoogle Scholar
  134. 134.
    Daly JJ, Hogan N, Perepezko EM et al (2005) Response to upper-limb robotics and functional neuromuscular stimulation following stroke. J Rehabil Res Dev 42:723–36CrossRefGoogle Scholar
  135. 135.
    Royer A, Rose M, He B (2011) Goal selection vs. process control while learning to use a brain-computer interface. J Neural Eng 8(3):036012CrossRefGoogle Scholar
  136. 136.
    Pfurtscheller G, Leeb R, Keinrath C, Friedman D, Neuper C, Guger C, Slater M (2006) Walking from thought. Brain Res 1071:145–152CrossRefGoogle Scholar
  137. 137.
    Schalk G, McFarland D, Hinterberger T, Birbaumer N, Wolpaw J (2004) BCI2000: a general purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 51:1034–1043CrossRefGoogle Scholar
  138. 138.
    Schalk G, Mellinger J (2010) A practical guide to brain-computer interfacing with BCI 2000. Springer, BerlinCrossRefGoogle Scholar
  139. 139.
    Wolpaw JR (2010) Brain-computer interface research comes of age: traditional assumptions meet emerging realities. J Motor Behav 42:351–353CrossRefGoogle Scholar
  140. 140.
    Pfurtscheller G, Flotzinger D, Kallcher J (1993) Brain-computer interface: a new communication device for handicapped persons. J Microcomput Appl 16:293–299CrossRefGoogle Scholar
  141. 141.
    Donchin E (1981) Presidential address, 1980. Surprise! … Surprise? Psychophysiology 18:493–513CrossRefGoogle Scholar
  142. 142.
    Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain–computer interface. IEEE Trans Rehabil Eng 8(2):174–179CrossRefGoogle Scholar
  143. 143.
    Townsend G, LaPallo BK, Boulay CB, Krusienski DJ, Frye GE, Hauser CK, Schwartz NE, Vaughan TM, Wolpaw JR, Seller EW (2010) A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121:1109–1120CrossRefGoogle Scholar
  144. 144.
    Martens SMM, Hill NJ, Farquhar J, Schölkopf B (2009) Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential. J Neural Eng 6:026003CrossRefGoogle Scholar
  145. 145.
    Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) An adaptive P300-based control system. J Neural Eng 8(3):036006. doi: 10.1088/1741-2560/8/3/036006 CrossRefGoogle Scholar
  146. 146.
    Treder MS, Blankertz B (2010) Covert attention and visual speller design in an ERP-based brain–computer interface. Behav Brain Funct 6(1):28CrossRefGoogle Scholar
  147. 147.
    Brunner P, Joshi S, Briskin S, Wolpaw JR, Bischof H, Schalk G (2010) Does the ‘P300’ speller depend on eye gaze? J Neural Eng 7(5):056013CrossRefGoogle Scholar
  148. 148.
    Liu Y, Zhou Z, Hu D (2011) Gaze independent brain–computer speller with covert visual search tasks. Clin Neurophysiol 122:1127–1136CrossRefGoogle Scholar
  149. 149.
    Hong B, Guo F, Liu T, Gao X, Gao S (2009) N200-speller using motion-onset visual response. ClinNeurophysiol 120(9):1658–66Google Scholar
  150. 150.
    Wang Y, Wang R, Gao X, Hong B, Gao S (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14(2):234–239CrossRefGoogle Scholar
  151. 151.
    Bin G, Gao X, Wang Y, Hong B, Gao S (2009a) VEP-based brain-computer interfaces: time, frequency, and code modulations. IEEE Comput Intell Mag 22–26Google Scholar
  152. 152.
    Cheng M, Gao X, Gao S, Xu D (2002) Design and Implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng 49(10):1181–1186CrossRefGoogle Scholar
  153. 153.
    Gao X, Xu D, Cheng M, Gao S (2003) A BCI-based environmental controller for the motiondisabled. IEEE Trans Neural Syst Rehabil Eng 11(2):137–140CrossRefGoogle Scholar
  154. 154.
    Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. J Neural Eng 6:046002. doi: 10.1088/1741-2560/6/4/046002 CrossRefGoogle Scholar
  155. 155.
    Guo F, Hong B, Gao X, Gao S (2008) A brain computer interface using motion-onset visual evoked potential. J Neural Eng 5(4):477–485CrossRefGoogle Scholar
  156. 156.
    Lee PL, Hsieh JC, Wu CH, Shyu KK, Chen SS, Yeh TC, Wu YT (2006) The brain computer interface using flash visual evoked potential and independent component analysis. Ann Biomed Eng 34(10):1641–1654CrossRefGoogle Scholar
  157. 157.
    Lee PL, Hsieh JC, Wu CH, Shyu KK, Wu YT (2008) Brain computer interface using flash onset and offset visual evoked potentials. Clin Neurophysiol 119(3):605–616CrossRefGoogle Scholar
  158. 158.
    Sutter EE (1984) The visual evoked response as a communication channel. IEEE Trans Biomed Eng 31(8):583Google Scholar
  159. 159.
    Hanagata J, Momose K (2002) A method for detecting gazed target using visual evoked potentials elicited by pseudorandom stimuli. In: Proceedings of 5th Asia Pacific conference on medical and biological engineering and 11th international conference on biomedical engineering (ICBME)Google Scholar
  160. 160.
    Momose K (2007) Evaluation of an eye gaze point detection method using VEP elicited by multi-pseudorandom stimulation for brain computer interface. In: Proceedings of 29th annual international conference of IEEE EMBSGoogle Scholar
  161. 161.
    Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S (2011) A high-speed BCI based on code modulation VEP. J Neural Eng 8:025015. doi: 10.1088/1741-2560/8/2/025015 CrossRefGoogle Scholar
  162. 162.
    Kluge T, Hartmann M (2007) Phase coherent detection of steady-stateevoked potentials: Experimental results and application to brain–computer interfaces. In: Proceedings of 3rd International IEEE EMBS neural engineering conference, pp 425–429Google Scholar
  163. 163.
    Wilson JJ, Palaniappan R (2009) Augmenting a SSVEP BCI through single cycle analysis and phase weighting. In: Proceedings of 4th international IEEE EMBS conference on neural engineering, Antalya, Turkey, pp 371–374Google Scholar
  164. 164.
    Jia C, Gao X, Hong B, Gao S (2011) Frequency and phase mixed coding in SSVEP-based brain–computer interface. IEEE Trans Biomed Eng 58(1):200–206CrossRefGoogle Scholar
  165. 165.
    Wang Y, Gao X, Hong B, Jia C, Gao S (2008) Brain-computer interfaces based on visual evoked potentials: feasibility of practical system designs. IEEE EMBS Mag 27(5):64–71Google Scholar
  166. 166.
    Wang Y, Gao X, Hong B, Jia C, Gao S (2008) Brain-computer interfaces based on visual evoked potentials. IEEE Eng Med Biol Mag 27(5):64–71CrossRefGoogle Scholar
  167. 167.
    Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kubler A (2008) An auditory brain-computer interface. J Neurosci Methods 167:43–50CrossRefGoogle Scholar
  168. 168.
    Hinterberger T, Hill J, Birbaumer N (2004) An auditory brain-computercommunication device. In: Paper presented at the IEEE International Workshop on Biomedical Circuits Systems, SingaporeGoogle Scholar
  169. 169.
    Pham M, Hinterberger T, Neumann N, Kubler A, Hofmayer N, Grether A, Wilhelm B, Vatine JJ, Birbaumer N (2005) An auditory brain-computer interface based on the self-regulation of slow cortical potentials. Neurorehabil Neural Repair 19:206–218CrossRefGoogle Scholar
  170. 170.
    Hill NJ, Lal TN, Bierig K, Birbaumer N, Scholkopf B (2004) Attentional modulation of auditory event-related potentials in a brain-computer interface. In: IEEE international workshop on biomedical circuits systems, SingaporeGoogle Scholar
  171. 171.
    Sellers EW, Donchin E (2006) A P300-based brain-computer-interface: initial tests by ALS patients. Clin Neurophysiol 117:538–548CrossRefGoogle Scholar
  172. 172.
    Furdea A, Halder S, Krusienski DJ (2009) An auditory oddball (P300) spelling system for brain-computer interfaces. Psychophysiology 46:617–625CrossRefGoogle Scholar
  173. 173.
    Guo J, Gao S, Hong B (2010) An auditory brain–computer interface using active mental response. IEEE Trans Neural Syst Rehabil Eng 18(3):230–235CrossRefGoogle Scholar
  174. 174.
    Kubler A, Furdea A, Halder S, Hammer EM, Nijboer F, Kotchoubey B (2009) A brain-computer interface controlled auditory event-related potential (P300) spelling system for locked-in patients. Disord Conscious 1157:90–100Google Scholar
  175. 175.
    Posner MI, Petersen SE (1990) The attention system of the human brain. Annu Rev Neurosci 13:25–42CrossRefGoogle Scholar
  176. 176.
    Posner MI, Dehane S (1994) Attentional networks. Trends Neurosci 17:75–9CrossRefGoogle Scholar
  177. 177.
    Desimone R, Duncan J (1995) Neural mechanisms of selective visual-attention. Annu Rev Neurosci 18:193–222CrossRefGoogle Scholar
  178. 178.
    Kelly SP, Lalor EC, Finucane C, McDarby G, Reilly RB (2005) Visual spatial attention control in an independentbrain–computer interface. IEEE Trans Biomed Eng 52:1588–96CrossRefGoogle Scholar
  179. 179.
    Kelly SP, Lalor EC, Reilly RB, FoxeJ J (2005) Visual spatial attention tracking using high-density SSVEP data for independent brain–computer communication. IEEE Trans Neural Syst Rehabil Eng 13:172–8CrossRefGoogle Scholar
  180. 180.
    Zhang D, Maye A, Gao X, Hong B, Engel AK, Gao S (2010) An independent brain–computer interface using covert non-spatial visual selective attention. J Neural Eng 7:016010CrossRefGoogle Scholar
  181. 181.
    Wolpaw JR, Ramoser H, McFarland DJ, Pfurtscheller G (1998) EEG-based communication: improved accuracy by response verification. IEEE Trans Rehabil Eng 6(3):326–333CrossRefGoogle Scholar
  182. 182.
    Pierce JR (1980) An introduction to information theory. Dover, New York, NYMATHGoogle Scholar
  183. 183.
    Shannon CE, Weaver W (1964) The mathematical theory of communication. University of Illinois Press, Urbana, ILGoogle Scholar
  184. 184.
    Curran EA, Stokes MJ (2003) Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain Cogn 51:326–336CrossRefGoogle Scholar
  185. 185.
    Babiloni F, Cincotti F, Lazzarini L, Millán J, Mouriño J, Varsta M, Heikkonen J, Bianchi L, Marciani MG (2000) Linear classification of low-resolution EEG patterns produced by imagined hand movements. IEEE Trans Rehabil Eng 8(2):186–188CrossRefGoogle Scholar
  186. 186.
    Penny WD, Roberts SJ, Curran EA, Stokes MJ (2000) EEG-based communication: a pattern recognition approach. IEEE Trans Rehabil Eng 8(2):214–215CrossRefGoogle Scholar
  187. 187.
    Penny WD, Roberts SJ (1999) EEG-based communication via dynamic neural network models. In: Proceedings of international joint conference on neural networks, CDROMGoogle Scholar
  188. 188.
    Royer AS, He B (2009) Goal selection vs. process control in a brain-computer interface based on sensorimotor rhythms. J Neural Eng 6(1):016005CrossRefGoogle Scholar
  189. 189.
    Ganguly K, Carmena JM (2009) Emergence of a stable cortical map for neuroprosthetic control. PLoS Biol 7:e1000153CrossRefGoogle Scholar
  190. 190.
    Qin L, He B (2005) A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications. J Neural Eng 2(4):65–72CrossRefGoogle Scholar
  191. 191.
    Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R. 'Thought'–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett. 2003 Nov 6 351(1):33–36Google Scholar
  192. 192.
    Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, Mehring C. Hand movement direction decoded from MEG and EEG. J Neurosci. 2008 Jan 23, 28(4):1000–1008CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bin He
    • 1
  • Shangkai Gao
    • 2
  • Han Yuan
    • 3
  • Jonathan R. Wolpaw
    • 4
  1. 1.Department of Biomedical EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Tsinghua UniversityBeijingChina
  3. 3.Laureate Institute for Brain ResearchTulsaUSA
  4. 4.Wadsworth Center, New York State Department of HealthAlbanyUSA

Personalised recommendations