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Brain Sensors and Signals

Chapter

Abstract

This chapter describes the sensors and brain signals that are commonly used for brain–computer interfacing. The two brain signal features that have been primarily used in humans, i.e., sensorimotor rhythms and the P300 evoked potential, are presented in detail. In addition, this chapter gives a practical introduction to EEG recordings, describes electrode materials and placement, and describes how to deal with the most important artifacts in EEG recordings. Finally, it provides an overview of BCI signal processing techniques that cover feature extraction and feature translation.

Keywords

Motor Imagery Computer Interface Brain Signal Beta Rhythm Translation Algorithm 
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.

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References

  1. 1.
    Allison, B.Z.: P3 or not P3: toward a better P300 BCI. PhD thesis, University of California, San Diego (2003) Google Scholar
  2. 2.
    Babiloni, F., Cincotti, F., Lazzarini, L., Millan, J., Mourino, J., Varsta, M., Heikkonen, J., Bianchi, L., Marciani, M.G.: Linear classification of low-resolution EEG patterns produced by imagined hand movements. IEEE Trans. Rehabil. Eng. 8(2), 186–188 (2000) Google Scholar
  3. 3.
    Ball, T., Kern, M., Mutschler, I., Aertsen, A., Schulze-Bonhage, A.: Signal quality of simultaneously recorded invasive and non-invasive EEG. NeuroImage 46(3), 708–716 (2009). doi: 10.1016/j.neuroimage.2009.02.028. http://www.hubmed.org/display.cgi?uids=19264143 Google Scholar
  4. 4.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32–R57 (2007). doi: 10.1088/1741-2560/4/2/R03 Google Scholar
  5. 5.
    Bayliss, J.D.: A flexible brain–computer interface. PhD thesis, University of Rochester, Rochester (2001). http://www.cs.rochester.edu/trs/robotics-trs.html
  6. 6.
    Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., Flor, H.: A spelling device for the paralysed. Nature 398(6725), 297–298 (1999) Google Scholar
  7. 7.
    Bullara, L.A., Agnew, W.F., Yuen, T.G., Jacques, S., Pudenz, R.H.: Evaluation of electrode array material for neural prostheses. Neurosurg. 5(6), 681–686 (1979) Google Scholar
  8. 8.
    Chatrian, G.E.: The mu rhythm. In: Handbook of Electroencephalography and Clinical Neurophysiology. The EEG of the Waking Adult, pp. 46–69. Elsevier, Amsterdam (1976) Google Scholar
  9. 9.
    Chin, C.M., Popovic, M.R., Thrasher, A., Cameron, T., Lozano, A., Chen, R.: Identification of arm movements using correlation of electrocorticographic spectral components and kinematic recordings. J. Neural Eng. 4(2), 146–158 (2007). doi: 10.1088/1741-2560/4/2/014 Google Scholar
  10. 10.
    Coyle, S., Ward, T., Markham, C., McDarby, G.: On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces. Physiol. Meas. 25(4), 815–822 (2004) Google Scholar
  11. 11.
    Coyle, S.M., Ward, T.E., Markham, C.M.: Brain–computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 4(3), 219–226 (2007). doi: 10.1088/1741-2560/4/3/007 Google Scholar
  12. 12.
    Crone, N.E., Miglioretti, D.L., Gordon, B., Sieracki, J.M., Wilson, M.T., Uematsu, S., Lesser, R.P.: Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. i. Alpha and beta event-related desynchronization. Brain 121 (12), 2271–2299 (1998) Google Scholar
  13. 13.
    Crone, N.E., Miglioretti, D.L., Gordon, B., Lesser, R.P.: Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. ii. Event-related synchronization in the gamma band. Brain 121 (12), 2301–2315 (1998) Google Scholar
  14. 14.
    Crone, N.E., Hao, L., Hart, J., Boatman, D., Lesser, R.P., Irizarry, R., Gordon, B.: Electrocorticographic gamma activity during word production in spoken and sign language. Neurol. 57(11), 2045–2053 (2001) Google Scholar
  15. 15.
    Donchin, E.: Presidential address, 1980. Surprise!...Surprise? Psychophysiol. 18(5), 493–513 (1981) Google Scholar
  16. 16.
    Donchin, E., Coles, M.: Is the P300 component a manifestation of context updating? Behav. Brain Sci. 11(3), 357–427 (1988) Google Scholar
  17. 17.
    Donchin, E., Smith, D.B.: The contingent negative variation and the late positive wave of the average evoked potential. Electroencephalogr. Clin. Neurophysiol. 29(2), 201–203 (1970) Google Scholar
  18. 18.
    Donchin, E., Heffley, E., Hillyard, S.A., Loveless, N., Maltzman, I., Ohman, A., Rösler, F., Ruchkin, D., Siddle, D.: Cognition and event-related potentials. ii. The orienting reflex and P300. Ann. N.Y. Acad. Sci. 425, 39–57 (1984) Google Scholar
  19. 19.
    Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain–computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000) Google Scholar
  20. 20.
    Donoghue, J., Nurmikko, A., Friehs, G., Black, M.: Development of neuromotor prostheses for humans. Suppl. Clin. Neurophysiol. 57, 592–606 (2004) Google Scholar
  21. 21.
    Donoghue, J.P., Nurmikko, A., Black, M., Hochberg, L.R.: Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. 579(3), 603–611 (2007). doi: 10.1113/jphysiol.2006.127209 Google Scholar
  22. 22.
    Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988) Google Scholar
  23. 23.
    Felton, E.A., Wilson, J.A., Williams, J.C., Garell, P.C.: Electrocorticographically controlled brain–computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J. Neurosurg. 106(3), 495–500 (2007) Google Scholar
  24. 24.
    Fisch, B.J.: Spehlmann’s EEG Primer, 2nd edn. Elsevier, Amsterdam (1991) Google Scholar
  25. 25.
    Freeman, W.J., Holmes, M.D., Burke, B.C., Vanhatalo, S.: Spatial spectra of scalp EEG and EMG from awake humans. Clin. Neurophysiol. 114, 1053–1068 (2003) Google Scholar
  26. 26.
    Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Rehabil. Eng. 11(2), 141–144 (2003) Google Scholar
  27. 27.
    Gastaut, H.: Etude electrocorticographique de la reactivite des rythmes rolandiques. Rev. Neurol. 87, 176–182 (1952) Google Scholar
  28. 28.
    Graimann, B., Huggins, J.E., Schlögl, A., Levine, S.P., Pfurtscheller, G.: Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram. IEEE Trans. Neural Syst. Rehabil. Eng. 11(3), 276–281 (2003) Google Scholar
  29. 29.
    Guger, C., Ramoser, H., Pfurtscheller, G.: Real-time EEG analysis with subject-specific spatial patterns for a brain–computer interface (BCI). IEEE Trans. Rehabil. Eng. 8(4), 447–456 (2000) Google Scholar
  30. 30.
    Gysels, E., Renevey, P., Celka, P.: SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain–computer interfaces. Signal Process. 85(11), 2178–2189 (2005) zbMATHGoogle Scholar
  31. 31.
    Hjorth, B.: Principles for transformation of scalp EEG from potential field into source distribution. J. Clin. Neurophysiol. 8(4), 391–396 (1991) CrossRefGoogle Scholar
  32. 32.
    Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., Donoghue, J.P.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006). doi: 10.1038/nature04970 Google Scholar
  33. 33.
    Hoffmann, U., Vesin, J.M., Ebrahimi, T., Diserens, K.: An efficient P300-based brain–computer interface for disabled subjects. J. Neurosci. Methods 167(1), 115–125 (2008). doi: 10.1016/j.jneumeth.2007.03.005 Google Scholar
  34. 34.
    Huan, N.J., Palaniappan, R.: Neural network classification of autoregressive features from electroencephalogram signals for brain–computer interface design. J. Neural Eng. 1(3), 142–150 (2004) Google Scholar
  35. 35.
    Jasper, H.H.: The ten twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1958) Google Scholar
  36. 36.
    Kostov, A., Polak, M.: Parallel man–machine training in development of EEG-based cursor control. IEEE Trans. Rehabil. Eng. 8(2), 203–205 (2000) Google Scholar
  37. 37.
    Kozelka, J.W., Pedley, T.A.: Beta and mu rhythms. J. Clin. Neurophysiol. 7, 191–207 (1990) Google Scholar
  38. 38.
    Krusienski, D.J., Schalk, G., McFarland, D.J., Wolpaw, J.R.: A mu-rhythm matched filter for continuous control of a brain–computer interface. IEEE Trans. Biomed. Eng. 54(2), 273–280 (2007). doi: 10.1109/TBME.2006.886661 Google Scholar
  39. 39.
    Kübler, A., Kotchoubey, B., Hinterberger, T., Ghanayim, N., Perelmouter, J., Schauer, M., Fritsch, C., Taub, E., Birbaumer, N.: The Thought Translation Device: a neurophysiological approach to communication in total motor paralysis. Exp. Brain Res. 124(2), 223–232 (1999) Google Scholar
  40. 40.
    Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T.M., Pawelzik, H., Schalk, G., McFarland, D.J., Birbaumer, N., Wolpaw, J.R.: Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurol. 64(10), 1775–1777 (2005). doi: 10.1212/01.WNL.0000158616.43002.6D Google Scholar
  41. 41.
    Lachaux, J.P., Fonlupt, P., Kahane, P., Minotti, L., Hoffmann, D., Bertrand, O., Baciu, M.: Relationship between task-related gamma oscillations and bold signal: new insights from combined fMRI and intracranial EEG. Hum. Brain Mapp. 28(12), 1368–1375 (2007). doi: 10.1002/hbm.20352 Google Scholar
  42. 42.
    LaConte, S.M., Peltier, S.J., Hu, X.P.: Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28(10), 1033–1044 (2007). doi: 10.1002/hbm.20326. http://www.hubmed.org/display.cgi?uids=17133383 Google Scholar
  43. 43.
    Lal, T.N., Schroder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.: Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51(6), 1003–1010 (2004) Google Scholar
  44. 44.
    Le, J., Gevins, A.: Method to reduce blur distortion from EEG’s using a realistic head model. IEEE Trans. Biomed. Eng. 40(6), 517–528 (1993) Google Scholar
  45. 45.
    Lebedev, M.A., Carmena, J.M., O’Doherty, J.E., Zacksenhouse, M., Henriquez, C.S., Principe, J.C., Nicolelis, M.A.: Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain–machine interface. J. Neurosci. 25(19), 4681–4693 (2005). doi: 10.1523/JNEUROSCI.4088-04.2005 Google Scholar
  46. 46.
    Leuthardt, E., Schalk, G., JR, J.W., Ojemann, J., Moran, D.: A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1(2), 63–71 (2004) Google Scholar
  47. 47.
    Leuthardt, E., Miller, K., Schalk, G., Rao, R., Ojemann, J.: Electrocorticography-based brain computer interface – the Seattle experience. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 194–198 (2006) Google Scholar
  48. 48.
    Leuthardt, E., Miller, K., Anderson, N., Schalk, G., Dowling, J., Miller, J., Moran, D., Ojemann, J.: Electrocorticographic frequency alteration mapping: a clinical technique for mapping the motor cortex. Neurosurg. 60, 260–270, discussion 270–271 (2007). doi: 10.1227/01.NEU.0000255413.70807.6E Google Scholar
  49. 49.
    Loeb, G.E., Walker, A.E., Uematsu, S., Konigsmark, B.W.: Histological reaction to various conductive and dielectric films chronically implanted in the subdural space. J. Biomed. Mater. Res. 11(2), 195–210 (1977). doi: 10.1002/jbm.820110206 Google Scholar
  50. 50.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), 1–1 (2007). doi: 10.1088/1741-2560/4/2/R01 Google Scholar
  51. 51.
    Makeig, S., Jung, T., Bell, A., Sejnowski, T.: Independent component analysis of electroencephalographic data. In: Advances in Neural Information Processing Systems, vol. 8, pp. 145–151. MIT Press, Cambridge (1996) Google Scholar
  52. 52.
    Margalit, E., Weiland, J., Clatterbuck, R., Fujii, G., Maia, M., Tameesh, M., Torres, G., D’Anna, S., Desai, S., Piyathaisere, D., Olivi, A., de Juan, E.J., Humayun, M.: Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia. J. Neurosci. Methods 123(2), 129–137 (2003) Google Scholar
  53. 53.
    Marple, S.L.: Digital Spectral Analysis: With Applications. Prentice–Hall, Englewood Cliffs (1987) Google Scholar
  54. 54.
    McFarland, D.J., Neat, G.W., Wolpaw, J.R.: An EEG-based method for graded cursor control. Psychobiol. 21, 77–81 (1993) Google Scholar
  55. 55.
    McFarland, D.J., Lefkowicz, T., Wolpaw, J.R.: Design and operation of an EEG-based brain–computer interface (BCI) with digital signal processing technology. Behav. Res. Methods Instrum. Comput. 29, 337–345 (1997) Google Scholar
  56. 56.
    McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial filter selection for EEG-based communication. Electroencephalogr. Clin. Neurophysiol. 103(3), 386–394 (1997) Google Scholar
  57. 57.
    McFarland, D.J., Miner, L.A., Vaughan, T.M., Wolpaw, J.R.: Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 12(3), 177–186 (2000) Google Scholar
  58. 58.
    McFarland, D., Anderson, C.W., Müller, K.R., Schlögl, A., Krusienski, D.J.: BCI meeting 2005 – workshop on BCI signal processing: feature extraction and translation. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 135–138 (2006) Google Scholar
  59. 59.
    McFarland, D.J., Krusienski, D.J., Sarnacki, W.A., Wolpaw, J.R.: Emulation of computer mouse control with a noninvasive brain–computer interface. J. Neural Eng. 5(2), 101–110 (2008). doi: 10.1088/1741-2560/5/2/001. http://www.hubmed.org/display.cgi?uids=18367779 Google Scholar
  60. 60.
    Mellinger, J., Schalk, G., Braun, C., Preissl, H., Rosenstiel, W., Birbaumer, N., Kübler, A.: An MEG-based brain–computer interface (BCI). NeuroImage 36(3), 581–593 (2007). doi: 10.1016/j.neuroimage.2007.03.019 Google Scholar
  61. 61.
    Millán, J. del R., Renkens, F., Mouriño, J., Gerstner, W.: Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 51(6), 1026–1033 (2004) Google Scholar
  62. 62.
    Miller, K., Leuthardt, E., Schalk, G., Rao, R., Anderson, N., Moran, D., Miller, J., Ojemann, J.: Spectral changes in cortical surface potentials during motor movement. J. Neurosci. 27, 2424–2432 (2007). doi: 10.1523/JNEUROSCI.3886-06.2007. http://www.jneurosci.org/cgi/content/abstract/27/9/2424 Google Scholar
  63. 63.
    Morgan, S.T., Hansen, J.C., Hillyard, S.A.: Selective attention to stimulus location modulates the steady-state visual evoked potential. Proc. Natl. Acad. Sci. USA 93(10), 4770–4774 (1996) Google Scholar
  64. 64.
    Müller, K., Blankertz, B.: Toward noninvasive brain–computer interfaces. IEEE Signal Process. Mag. 23(5), 126–128 (2006) Google Scholar
  65. 65.
    Müller, K.R., Anderson, C.W., Birch, G.E.: Linear and nonlinear methods for brain–computer interfaces. IEEE Trans. Rehabil. Eng. 11(2), 165–169 (2003) Google Scholar
  66. 66.
    Müller, K.R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., Blankertz, B.: Machine learning for real-time single-trial EEG-analysis: from brain–computer interfacing to mental state monitoring. J. Neurosci. Methods 167(1), 82–90 (2008). doi: 10.1016/j.jneumeth.2007.09.022. http://www.hubmed.org/display.cgi?uids=18031824 Google Scholar
  67. 67.
    Musallam, S., Corneil, B.D., Greger, B., Scherberger, H., Andersen, R.A.: Cognitive control signals for neural prosthetics. Science 305(5681), 258–262 (2004). doi: 10.1126/science.1097938 Google Scholar
  68. 68.
    Neshige, R., Murayama, N., Tanoue, K., Kurokawa, H., Igasaki, T.: Optimal methods of stimulus presentation and frequency analysis in P300-based brain–computer interfaces for patients with severe motor impairment. Suppl. Clin. Neurophysiol. 59, 35–42 (2006) Google Scholar
  69. 69.
    Niedermeyer, E.: The normal EEG of the waking adult. In: Niedermeyer, E., Lopes da Silva, F.H. (eds.) Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 4th edn., pp. 149–173. Williams and Wilkins, Baltimore (1999) Google Scholar
  70. 70.
    Pfurtscheller, G.: EEG event-related desynchronization (ERD) and event-related synchronization (ERS). In: Niedermeyer, E., Lopes da Silva, F.H. (eds.) Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 4th edn., pp. 958–967. Williams and Wilkins, Baltimore (1999) Google Scholar
  71. 71.
    Pfurtscheller, G., Berghold, A.: Patterns of cortical activation during planning of voluntary movement. Electroencephalogr. Clin. Neurophysiol. 72, 250–258 (1989) Google Scholar
  72. 72.
    Pfurtscheller, G., Neuper, C.: Motor imagery activates primary sensorimotor area in humans. Neurosci. Lett. 239, 65–68 (1997) Google Scholar
  73. 73.
    Pfurtscheller, G., Flotzinger, D., Kalcher, J.: Brain–computer interface – a new communication device for handicapped persons. J. Microcomput. Appl. 16, 293–299 (1993) Google Scholar
  74. 74.
    Pfurtscheller, G., Neuper, C., Kalcher, J.: 40-Hz oscillations during motor behavior in man. Neurosci. Lett. 164(1–2), 179–182 (1993) Google Scholar
  75. 75.
    Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr. Clin. Neurophysiol. 103(6), 642–651 (1997) Google Scholar
  76. 76.
    Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C.: Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292(3), 211–214 (2000) Google Scholar
  77. 77.
    Pfurtscheller, G., Graimann, B., Huggins, J.E., Levine, S.P., Schuh, L.A.: Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement. Clin. Neurophysiol. 114(7), 1226–1236 (2003) Google Scholar
  78. 78.
    Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giove, S., Silvoni, S., Palmas, G., Beverina, F.: P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin. Neurophysiol. 117(3), 531–537 (2006). doi: 10.1016/j.clinph.2005.07.024 Google Scholar
  79. 79.
    Pistohl, T., Ball, T., Schulze-Bonhage, A., Aertsen, A., Mehring, C.: Prediction of arm movement trajectories from ECoG-recordings in humans. J. Neurosci. Methods 167(1), 105–114 (2008) Google Scholar
  80. 80.
    Pritchard, W.S.: Psychophysiology of P300. Psychol. Bull. 89(3), 506–540 (1981) Google Scholar
  81. 81.
    Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4), 441–446 (2000) Google Scholar
  82. 82.
    Ramsey, N.F., van de Heuvel, M.P., Kho, K.H., Leijten, F.S.: Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 214–217 (2006). http://www.hubmed.org/display.cgi?uids=16792297 Google Scholar
  83. 83.
    Sanchez, J.C., Gunduz, A., Carney, P.R., Principe, J.C.: Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. J. Neurosci. Methods 167(1), 63–81 (2008). doi: 10.1016/j.jneumeth.2007.04.019 Google Scholar
  84. 84.
    Santhanam, G., Ryu, S.I., Yu, B.M., Afshar, A., Shenoy, K.V.: A high-performance brain–computer interface. Nature 442(7099), 195–198 (2006). doi: 10.1038/nature04968 Google Scholar
  85. 85.
    Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., Wolpaw, J.: BCI2000: a general-purpose brain–computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004) Google Scholar
  86. 86.
    Schalk, G., Kubánek, J., Miller, K.J., Anderson, N.R., Leuthardt, E.C., Ojemann, J.G., Limbrick, D., Moran, D., Gerhardt, L.A., Wolpaw, J.R.: Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J. Neural Eng. 4(3), 264–275 (2007). doi: 10.1088/1741-2560/4/3/012 Google Scholar
  87. 87.
    Schalk, G., Miller, K.J., Anderson, N.R., Wilson, J.A., Smyth, M.D., Ojemann, J.G., Moran, D.W., Wolpaw, J.R., Leuthardt, E.C.: Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng. 5(1), 75–84 (2008). doi: 10.1088/1741-2560/5/1/008 Google Scholar
  88. 88.
    Sellers, E.W., Donchin, E.: A P300-based brain–computer interface: initial tests by ALS patients. Clin. Neurophysiol. 117(3), 538–548 (2006). doi: 10.1016/j.clinph.2005.06.027 Google Scholar
  89. 89.
    Sellers, E.W., Kübler, A., Donchin, E.: Brain–computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 Speller. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 221–224 (2006) Google Scholar
  90. 90.
    Sellers, E.W., Krusienski, D.J., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol. Psychol. 73(3), 242–252 (2006). doi: 10.1016/j.biopsycho.2006.04.007 Google Scholar
  91. 91.
    Serby, H., Yom-Tov, E., Inbar, G.F.: An improved P300-based brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1), 89–98 (2005) Google Scholar
  92. 92.
    Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M., Donoghue, J.: Instant neural control of a movement signal. Nature 416(6877), 141–142 (2002) Google Scholar
  93. 93.
    Shain, W., Spataro, L., Dilgen, J., Haverstick, K., Retterer, S., Isaacson, M., Saltzman, M., Turner, J.: Controlling cellular reactive responses around neural prosthetic devices using peripheral and local intervention strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 186–188 (2003) Google Scholar
  94. 94.
    Sharbrough, F., Chatrian, G., Lesser, R., Luders, H., Nuwer, M., Picton, T.: American electroencephalographic society guidelines for standard electrode position nomenclature. Electroencephalogr. Clin. Neurophysiol. 8, 200–202 (1991) Google Scholar
  95. 95.
    Shenoy, K., Meeker, D., Cao, S., Kureshi, S., Pesaran, B., Buneo, C., Batista, A., Mitra, P., Burdick, J., Andersen, R.: Neural prosthetic control signals from plan activity. Neurorep. 14(4), 591–596 (2003) Google Scholar
  96. 96.
    Sinai, A., Bowers, C.W., Crainiceanu, C.M., Boatman, D., Gordon, B., Lesser, R.P., Lenz, F.A., Crone, N.E.: Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming. Brain 128(7), 1556–1570 (2005). doi: 10.1093/brain/awh491 Google Scholar
  97. 97.
    Sitaram, R., Caria, A., Birbaumer, N.: Hemodynamic brain–computer interfaces for communication and rehabilitation. Neural Netw. 22(9), 1320–1328 (2009). doi: 10.1016/j.neunet.2009.05.009. http://www.hubmed.org/display.cgi?uids=19524399 Google Scholar
  98. 98.
    Sitaram, R., Caria, A. Veit, R., Gaber, T., Rota, G., Kübler, A., Birbaumer, N.: fMRI brain–computer interface: a tool for neuroscientific research and treatment. Comput. Intell. Neurosci. 2007, Article ID 25487 (10 pages) (2007). doi: 10.1155/2007/25487 Google Scholar
  99. 99.
    Staba, R.J., Wilson, C.L., Bragin, A., Fried, I., Engel, J.: Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J. Neurophysiol. 88(4), 1743–1752 (2002) Google Scholar
  100. 100.
    Stice, P., Muthuswamy, J.: Assessment of gliosis around moveable implants in the brain. J. Neural Eng. 6(4), 046004 (2009). doi: 10.1088/1741-2560/6/4/046004 Google Scholar
  101. 101.
    Sutter, E.E.: The brain response interface: communication through visually guided electrical brain responses. J. Microcomput. Appl. 15, 31–45 (1992) Google Scholar
  102. 102.
    Sutton, S., Braren, M., Zubin, J., John, E.R.: Evoked-potential correlates of stimulus uncertainty. Science 150(700), 1187–1188 (1965) Google Scholar
  103. 103.
    Taylor, D.M., Tillery, S.I., Schwartz, A.B.: Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002) Google Scholar
  104. 104.
    Toro, C., Cox, C., Friehs, G., Ojakangas, C., Maxwell, R., Gates, J.R., Gumnit, R.J., Ebner, T.J.: 8–12 Hz rhythmic oscillations in human motor cortex during two-dimensional arm movements: evidence for representation of kinematic parameters. Electroencephalogr. Clin. Neurophysiol. 93(5), 390–403 (1994) Google Scholar
  105. 105.
    Turner, J.N., Ancin, H., Becker, D., Szarowski, D.H., Holmes, M., O’Connor, N., Wang, M., Holmes, T.J., Roysam, B.: Automated image analysis technologies for biological 3-d light microscopy. Int. J. Imaging Syst. Technol., Spec. Issue Microsc. 8, 240–254 (1997) Google Scholar
  106. 106.
    Vaughan, T.M., McFarland, D.J., Schalk, G., Sarnacki, W.A., Krusienski, D.J., Sellers, E.W., Wolpaw, J.R.: The Wadsworth BCI research and development program: at home with BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 229–233 (2006) Google Scholar
  107. 107.
    Walter, W.G., Cooper, R., Aldridge, V.J., McCallum, W.C., Winter, A.L.: Contingent negative variation: an electric sign of sensorimotor association and expectancy in the human brain. Nature 203, 380–384 (1964) Google Scholar
  108. 108.
    Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., Birbaumer, N.: Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage 19(3), 577–586 (2003) Google Scholar
  109. 109.
    Weiskopf, N., Mathiak, K., Bock, S.W., Scharnowski, F., Veit, R., Grodd, W., Goebel, R., Birbaumer, N.: Principles of a brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004) Google Scholar
  110. 110.
    Weiskopf, N., Scharnowski, F., Veit, R., Goebel, R., Birbaumer, N., Mathiak, K.: Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J. Physiol. Paris 98(4–6), 357–373 (2004). doi: 10.1016/j.jphysparis.2005.09.019 Google Scholar
  111. 111.
    Wilson, J., Felton, E., Garell, P., Schalk, G., Williams, J.: ECoG factors underlying multimodal control of a brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 246–250 (2006) Google Scholar
  112. 112.
    Wolpaw, J., Birbaumer, N.: Brain–computer interfaces for communication and control. In: Selzer, M., Clarke, S., Cohen, L., Duncan, P., Gage, F. (eds.) Textbook of Neural Repair and Rehabilitation; Neural Repair and Plasticity, pp. 602–614. Cambridge University Press, Cambridge (2006) Google Scholar
  113. 113.
    Wolpaw, J.R., McFarland, D.J.: Multichannel EEG-based brain–computer communication. Electroencephalogr. Clin. Neurophysiol. 90(6), 444–449 (1994) Google Scholar
  114. 114.
    Wolpaw, J.R., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc. Natl. Acad. Sci. USA 101(51), 17849–17854 (2004). doi: 10.1073/pnas.0403504101. http://www.hubmed.org/display.cgi?uids=15585584 Google Scholar
  115. 115.
    Wolpaw, J., McFarland, D., Cacace, A.: Preliminary studies for a direct brain-to-computer parallel interface. In: Projects for Persons with Disabilities. IBM Technical Symposium, pp. 11–20 (1986) Google Scholar
  116. 116.
    Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain–computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78(3), 252–259 (1991) Google Scholar
  117. 117.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Electroencephalogr. Clin. Neurophysiol. 113(6), 767–791 (2002) Google Scholar
  118. 118.
    Yoo, S.S., Fairneny, T., Chen, N.K., Choo, S.E., Panych, L.P., Park, H., Lee, S.Y., Jolesz, F.A.: Brain–computer interface using fMRI: spatial navigation by thoughts. Neurorep. 15(10), 1591–1595 (2004) Google Scholar
  119. 119.
    Yuen, T.G., Agnew, W.F., Bullara, L.A.: Tissue response to potential neuroprosthetic materials implanted subdurally. Biomaterials 8(2), 138–141 (1987) Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.Wadsworth CenterNew York State Department of HealthAlbanyUSA
  2. 2.Institute of Medical Psychology & Behavioral NeurobiologyUniversity of TübingenTübingenGermany

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