Advertisement

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

  • Kusuma Mohanchandra
  • Snehanshu Saha
  • G. M. Lingaraju
Part of the Intelligent Systems Reference Library book series (ISRL, volume 74)

Abstract

EEG based brain computer interface has emerged as a hot spot in the study of neuroscience, machine learning and rehabilitation in the recent years. A BCI provides a platform for direct communication between a human brain and a computer without the normal neurophysiology pathways. The electrical signals in the brain, because of their fast response to cognitive processes are most suitable as non-motor controlled mediation between the human and a computer. It can serve as a communication and control channel for different applications. Though the primary goal is to restore communication in severely paralyzed population, the BCI for speech communication fetches recognition in a variety of non-medical fields, the silent speech communication, cognitive biometrics and synthetic telepathy to name a few. A survey of diverse applications and principles of the BCI technology used for speech communication is discussed in this chapter. An ample evidence of speech communication used by “locked-in” patients is specified. Through the aid of assistive computer technology, they were able to pen their memoir. The current state-of-the-art techniques and control signals used for speech communication is described in brief. Possible future research directions are discussed. A comparison of indirect and direct methods of BCI speech production is shown. The direct method involves capturing the brain signals of the intended speech or speech imagery, processes the signals to predict the speech and synthesizes the speech production in real-time. There is enough evidence that the direct speech prediction from the neurological signals is a promising technology with fruitful results and challenging issues.

Keywords

Brain computer interface Locked-in syndrome Electroencephalography Silent communication Imagined speech 

References

  1. 1.
    Azar, A.T., Balas, V.E., Olariu, T.: Classification of EEG-based brain–computer interfaces. Springer International Publishing in Advanced Intelligent Computational Technologies and Decision Support Systems, pp. 97–106. Springer International Publishing, Switzerland (2014)Google Scholar
  2. 2.
    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 (2007)Google Scholar
  3. 3.
    Bauby, J.D.: The diving bell and the butterfly: a memoir of life in death. Translated from the French by Jeremy Leggatt. Alfred A. Knopf, Inc, New York (1997)Google Scholar
  4. 4.
    Bauer, G., Gerstenbrand, F., Rumpl, E.: Varieties of the locked-in syndrome. J. Neurol. 221(2), 77–91 (1979)CrossRefGoogle Scholar
  5. 5.
    Birbaumer, N., Kübler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Flor, H.: The thought translation device (TTD) for completely paralyzed patients. IEEE Trans. Rehabil. Eng. 8(2), 191 (2000)CrossRefGoogle Scholar
  6. 6.
    Birbaumer, N., Hinterberger, T., Kubler, A., Neumann, N.: The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 120–123 (2003)CrossRefGoogle Scholar
  7. 7.
    Birbaumer, N., Cohen, L.G.: Brain–computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579(3), 621–636 (2007)CrossRefGoogle Scholar
  8. 8.
    Blank, S.C., Scott, S.K., Murphy, K., Warburton, E., Wise, R.J.: Speech production: Wernicke Broca and beyond. Brain 125(8), 1829–1838 (2002)Google Scholar
  9. 9.
    Bogue, R.: Brain-computer interfaces: control by thought. Ind. Robot. Int. J. 37(2), 126–132 (2010)CrossRefGoogle Scholar
  10. 10.
    Brain Master Technologies Inc. (n.d). The international 10–20 system (electronic print). http://www.brainmaster.com/generalinfo/electrodeuse/eegbands/1020/1020.html. Accessed 10 Nov 2013
  11. 11.
    Brigham, K., Kumar, B.V.: Imagined speech classification with EEG signals for silent communication: a preliminary investigation into synthetic telepathy. In: The 4th International IEEE Conference on Bioinformatics and Biomedical Engineering (iCBBE), pp. 1–4. Chengdu, China, 18–20 June 2010Google Scholar
  12. 12.
    Brumberg, J.S., Kennedy, P.R., Guenther, F.H.: Artificial speech synthesizer control by brain-computer interface. In: Proceedings of the 10th Annual Conference of the International Speech Communication Association (INTERSPEECH 2009), pp. 636–639. International Speech Communication Association, Brighton, U.K., 6–10 Sept 2009Google Scholar
  13. 13.
    Brumberg, J.S., Guenther, F.H.: Development of speech prostheses: current status and recent advances. Expert Rev. Med. Devices 7(5), 667–679 (2010)CrossRefGoogle Scholar
  14. 14.
    Brumberg, J.S., Nieto-Castanon, A., Kennedy, P.R., Guenther, F.H.: Brain–computer interfaces for speech communication. Speech Commun. 52(4), 367–379 (2010)Google Scholar
  15. 15.
    Brumberg, J.S., Wright, E.J., Andreasen, D.S., Guenther, F.H., Kennedy, P.R.: Classification of intended phoneme production from chronic intracortical microelectrode recordings in speech-motor cortex. Fronti. Neurosci. 5(65), 1–14 (2011)Google Scholar
  16. 16.
    Cipresso, P., Carelli, L., Solca, F., Meazzi, D., Meriggi, P., Poletti, B., Riva, G.: The use of P300-based BCIs in amyotrophic lateral sclerosis: from augmentative and alternative communication to cognitive assessment. Brain Behav. 2(4), 479–498 (2012)CrossRefGoogle Scholar
  17. 17.
    DaSalla, C.S., Kambara, H., Sato, M., Koike, Y.: Single-trial classification of vowel speech imagery using common spatial patterns. Neural Netw. 22(9), 1334–1339 (2009)Google Scholar
  18. 18.
    Denby, B., Schultz, T., Honda, K., Hueber, T., Gilbert, J.M., Brumberg, J.S.: Silent speech interfaces. Speech Commun. 52(4), 270–287 (2010)CrossRefGoogle Scholar
  19. 19.
    Discover magazine: The army’s bold plan to turn soldiers into telepaths. http://discovermagazine.com/2011/apr/15-armys-bold-plan-turn-soldiers-into-telepaths#.UZe6-9isOSo. Accessed 10 Nov 2013
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    D’Zmura, M., Deng, S., Lappas, T., Thorpe, S., Srinivasan, R.: Toward EEG sensing of imagined speech. In: Jacko J.A. (ed.) Human-Computer Interaction New Trends, Part I, HCII 2009, LNCS 5610, pp. 40–48. Springer, Berlin (2009)Google Scholar
  22. 22.
    D’Zmura, M.: MURI: synthetic telepathy. MURI: Imagined Speech & Intended Direction. http://cnslab.ss.uci.edu/muri/research.html. Accessed 10 Nov 2013
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Foster, J.B.: Locked-in syndrome: advances in communication spur rehabilitation. Psychiatr. Times (issue of Appl. Neurol.) 3(1) (2007 January). http://www.jordanafoster.com/article.asp?a=/neuro/20070101_Locked-in_Syndrome. Accessed 10 Nov 2013
  25. 25.
    Gosseries, O., Bruno, M.A, Vanhaudenhuyse, A., Laureys, S., Schnakers, C.: Consciousness in the locked-in syndrome. The neurology of consciousness: Cognitive neuroscience and neuropathology, 191–203. Academic Press, Oxford (2009)Google Scholar
  26. 26.
    Guan C, Thulasidas M, Wu J (2004) High performance P300 speller for brain-computer interface. In: The 2004 IEEE International Workshop on Biomedical Circuits and Systems, 1–3 Dec 2004, Singapore, pp. S3–S5. doi: 10.1109/BIOCAS.2004.1454079
  27. 27.
    Guenther, F.H., Brumberg, J.S., Wright, E.J., Nieto-Castanon, A., Tourville, J.A., Panko, M., Kennedy, P.R.: A wireless brain-machine interface for real-time speech synthesis. PloS one 4(12), e8218 (2009)Google Scholar
  28. 28.
    Guenther, F.H., Brumberg, J.S.: Brain-machine interfaces for real-time speech synthesis. In: The 2011 Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBC, 30 Aug–03 Sep 2011, Boston, MA, USA, pp. 5360–5363, 2011 AugustGoogle Scholar
  29. 29.
    Guger, C., Schlogl, A., Neuper, C., Walterspacher, D., Strein, T., Pfurtscheller, G.: Rapid prototyping of an EEG-based brain-computer interface (BCI). IEEE Trans. Neural Syst. Rehabil. Eng. 9(1), 49–58 (2001)CrossRefGoogle Scholar
  30. 30.
    Herrmann, C.S., Demiralp, T.: Human EEG gamma oscillations in neuropsychiatric disorders. Clin. Neurophysiol. 116(12), 2719–2733 (2005)CrossRefGoogle Scholar
  31. 31.
    Hinojosa, J.A., Martı́n-Loeches, M., Rubia, F.J.: Event-related potentials and semantics: an overview and an integrative proposal. Brain Lang 78(1), 128–139 (2001)Google Scholar
  32. 32.
    Hinterberger, T., Kübler, A., Kaiser, J., Neumann, N., Birbaumer, N.: A brain–computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Clin. Neurophysiol. 114(3), 416–425 (2003)Google Scholar
  33. 33.
    Hinterberger, T., Mellinger, J., Birbaumer, N.: The thought translation device: structure of a multimodal brain-computer communication system. In: The 2003 First International IEEE EMBS Conference on Neural Engineering, 20–22 March 2003, Capri Island, Italy, pp. 603–606 (2003). doi: 10.1109/CNE.2003.1196293
  34. 34.
    Hinterberger, T., Houtkooper, J.M., Kotchoubey, B.: Effects of feedback control on slow cortical potentials and random events. In: The 2004 Parapsychological Association Convention, 05–08 August 2004, Vienna, pp. 39–50 (2004)Google Scholar
  35. 35.
    Johnson, R.: On the neural generators of the P300 component of the event-related potential. Psychophysiology 30(1), 90–97 (1993)CrossRefGoogle Scholar
  36. 36.
    Jorgensen, C., Lee, D.D., Agabont, S.: Sub auditory speech recognition based on EMG signals. In: The IEEE International Joint Conference on Neural Networks, 20–24 July 2003, Portland, OR, vol. 4, pp. 3128–3133 (2003). doi: 10.1109/IJCNN.2003.1223240
  37. 37.
    Jorgensen, C., Binsted, K.: Web browser control using EMG based sub vocal speech recognition. In: The IEEE 38th Annual Hawaii International Conference on System Sciences, HICSS’05, 3–4 January 2005, Hilton Waikoloa Village, Island of Hawaii (Big Island), vol. 09, pp. 294c–294c (2005). doi: 10.1109/HICSS.2005.683
  38. 38.
    Kennedy, P.R., Bakay, R.A., Moore, M.M., Adams, K., Goldwaithe, J.: Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8(2), 198–202 (2000)CrossRefGoogle Scholar
  39. 39.
    Krepki, R., Blankertz, B., Curio, G., Müller, K.R.: The Berlin Brain-Computer Interface (BBCI)–towards a new communication channel for online control in gaming applications. Multimed. Tools Appl. 33(1), 73–90 (2007)CrossRefGoogle Scholar
  40. 40.
    Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. J. Neurosci. Methods 167(1), 15–21 (2008)CrossRefGoogle Scholar
  41. 41.
    Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T.M., Pawelzik, H., Schalk, G., Wolpaw, J.R.: Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64(10), 1775–1777 (2005)CrossRefGoogle Scholar
  42. 42.
    Kusuma, M., Lingaraju, G.M., Prashanth, K., Vinay, K.: Using brain waves as new biometric feature for authenticating a computer user in real-time. Int. J. Biometrics Bioinf. 7(1), 49 (2013)Google Scholar
  43. 43.
    Laureys, S., Pellas, F., Van Eeckhout, P., Ghorbel, S., Schnakers, C., Perrin, F., Goldman, S.: The locked-in syndrome: what is it like to be conscious but paralyzed and voiceless?. Prog. Brain Res. 150, 495–611 (2005)Google Scholar
  44. 44.
    Laureys, S., Celesia, G., Cohadon, G., Lavrijsen, F., León-Carrión, J., Sannita, W.G., Dolce, G.: Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. 8(1), 68 (2010)CrossRefGoogle Scholar
  45. 45.
    Lee, PLWuCH, Hsieh, J.C., Wu, Y.T.: Visual evoked potential actuated brain computer interface: a brain-actuated cursor system. Electron. Lett. 41(15), 832–834 (2005)CrossRefGoogle Scholar
  46. 46.
    Lee, P.L., Hsieh, J.C., Wu, C.H., Shyu, K.K., Wu, Y.T.: Brain computer interface using flash onset and offset visual evoked potentials. Clin. Neurophysiol. 119(3), 605–616 (2008)CrossRefGoogle Scholar
  47. 47.
    Lee, P.L., Sie, J.J., Liu, Y.J., Wu, C.H., Lee, M.H., Shus, C.H., Shyu, K.K.: An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system. Ann. Biomed. Eng. 38(7), 2383–2397 (2010)CrossRefGoogle Scholar
  48. 48.
    Leuthardt, E.C., Schalk, G., Wolpaw, J.R., Ojemann, J.G., Moran, D.W.: A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1(2), 63 (2004)CrossRefGoogle Scholar
  49. 49.
    Leuthardt, E.C., Miller, K.J., Schalk, G., Rao, R.P., Ojemann, J.G.: Electrocorticography-based brain computer interface-the Seattle experience. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 194–198 (2006)Google Scholar
  50. 50.
    Leuthardt, E.C., Gaona, C., Sharma, M., Szrama, N., Roland, J., Freudenberg, Z., Schalk, G.: Using the electrocorticographic speech network to control a brain–computer interface in humans. J. Neural Eng. 8(3), 036004 (2011)CrossRefGoogle Scholar
  51. 51.
    Lutzenberger, W., Elbert, T., Rockstroh, B., Birbaumer, N.: Biofeedback produced slow brain potentials and task performance. Biol. Psychol. 14(1), 99–111 (1982)CrossRefGoogle Scholar
  52. 52.
    Marcel, S., Millán, J.D.R.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 743–752 (2007)CrossRefGoogle Scholar
  53. 53.
    Mason, S.G., Birch, G.E.: A general framework for brain-computer interface design. IEEE Trans. Neural Syst. Rehabil. Eng. 11(1), 70–85 (2003)CrossRefGoogle Scholar
  54. 54.
    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)CrossRefGoogle Scholar
  55. 55.
    Mozersky, J.: Locked in: a young woman’s battle with stroke. Golden Dog Press, Canada, Dundurn (2000)Google Scholar
  56. 56.
    NASA.: NASA develops system to computerize silent ‘subvocal speech’ (March 17 2004). http://www.nasa.gov/home/hqnews/2004/mar/HQ_04093_subvocal_speech.html. Accessed 10 Nov 2013
  57. 57.
    Neuper, C., Wörtz, M., Pfurtscheller, G.: ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog. Brain Res. 159, 211–222 (2006)CrossRefGoogle Scholar
  58. 58.
    Neuper, C., Scherer, R., Wriessnegger, S., Pfurtscheller, G.: Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin. Neurophysiol. 120(2), 239–247 (2009)CrossRefGoogle Scholar
  59. 59.
    Nicolas-Alonso, L.F., Gomez-Gil, J.: Brain computer interfaces—a review. Sensors 12(2), 1211–1279 (2012)CrossRefGoogle Scholar
  60. 60.
    Nijboer, F., Sellers, E.W., Mellinger, J., Jordan, M.A., Matuz, T., Furdea, A., Kübler, A.: A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 119(8), 1909–1916 (2008)Google Scholar
  61. 61.
    Palaniappan, R.: Utilizing gamma band to improve mental task based brain-computer interface design. IEEE Trans. Neural Syst. Rehabil. Eng. 14(3), 299–303 (2006)CrossRefGoogle Scholar
  62. 62.
    Palaniappan, R., Mandic, D.P.: EEG based biometric framework for automatic identity verification. J. VLSI Signal Process. Syst. Signal Image Video Technol. 49(2), 243–250 (2007)CrossRefGoogle Scholar
  63. 63.
    Palaniappan, R.: Two-stage biometric authentication method using thought activity brain waves. Int. J. Neural Syst. 18(01), 59–66 (2008)CrossRefGoogle Scholar
  64. 64.
    Perelmouter, J., Birbaumer, N.: A binary spelling interface with random errors. IEEE Trans. Rehabil. Eng. 8(2), 227–232 (2000)CrossRefGoogle Scholar
  65. 65.
    Peterson, N.N., Schroeder, C.E., Arezzo, J.C.: Neural generators of early cortical somatosensory evoked potentials in the awake monkey. Electroencephalogr. Clin. Neurophysiol./Evoked Potentials Sect. 96(3), 248–260 (1995)CrossRefGoogle Scholar
  66. 66.
    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)CrossRefGoogle Scholar
  67. 67.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001). doi: 10.1109/5.939829 CrossRefGoogle Scholar
  68. 68.
    Pfurtscheller, G., Neuper, C., Müller, G.R., Obermaier, B., Krausz, G., Schlögl, A., Schrank, C.: Graz-BCI: state of the art and clinical applications. IEEE Trans. Neural Syst. Rehabil. Eng. (a publication of the IEEE Eng. Med. Biol. Soc.) 11(2), 177–180 (2003)Google Scholar
  69. 69.
    Poulos, M., Rangoussi, M., Alexandris, N., Evangelou, A.: Person identification from the EEG using nonlinear signal classification. Methods Inf. Med. 41(1), 64–75 (2002)Google Scholar
  70. 70.
    Ravi, K.V.R., Palaniappan, R.: Recognising individuals using their brain patterns. In: The IEEE Third International Conference on Information Technology and Applications (ICITA), 4–7 July 2005, Sydney, NSW, vol. 2, pp. 520–523 (2005). doi: 10.1109/ICITA.2005.282
  71. 71.
    Ravi, K.V.R., Palaniappan, R.: Leave-one-out authentication of persons using 40 Hz EEG oscillations. In: The IEEE International Conference on Computer as a Tool (EUROCON), 21–24 November 2005, Belgrade, Serbia and Montenegro, vol. 2, pp. 1386–1389 (2005)Google Scholar
  72. 72.
    Revett, K., Deravi, F., Sirlantzis, K.: Biosignals for user authentication-towards cognitive biometrics?. In: The IEEE International Conference on Emerging Security Technologies (EST), 6–8 September 2010, Canterbury, United Kingdom, pp. 71–76 (2010)Google Scholar
  73. 73.
    Rockstroh, B., Elbert, T., Lutzenberger, W., Birbaumer, N.: The effects of slow cortical potentials on response speed. Psychophysiology 19(2), 211–217 (1982)CrossRefGoogle Scholar
  74. 74.
    Schlosser, R.: Roles of speech output in augmentative and alternative communication: narrative review. Augment. Altern. Commun. 19(1), 5–27 (2003)CrossRefGoogle Scholar
  75. 75.
    Sellers, E.W., Donchin, E.: A P300-based brain–computer interface: initial tests by ALS patients. Clin. Neurophysiol. 117(3), 538–548 (2006)CrossRefGoogle Scholar
  76. 76.
    Smith, E., Delargy, M.: Locked-in syndrome. Br. Med. J. 330(7488), 406 (2005)CrossRefGoogle Scholar
  77. 77.
    Stephen, H.: Read a preview from my brief history (n d). http://www.hawking.org.uk. Accessed 10 Nov 2013
  78. 78.
    Suppes, P., Lu, Z.L., Han, B.: Brain wave recognition of words. Proc. Natl. Acad. Sci. U S A 94(26), 14965–14969 (1997)CrossRefGoogle Scholar
  79. 79.
    Suppes, P., Han, B.: Brain-wave representation of words by superposition of a few sine waves. Proc. Natl. Acad. Sci. 97(15), 8738–8743 (2000)CrossRefGoogle Scholar
  80. 80.
    Sur, S., Sinha, V.K.: Event-related potential: an overview. Ind. Psychiatr. J. 18(1), 70–73 (2009). doi: 10.4103/0972-6748.57865 CrossRefGoogle Scholar
  81. 81.
    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
  82. 82.
    Wang, Y., Gao, X., Hong, B., Gao, S.: Practical designs of brain–computer interfaces based on the modulation of EEG rhythms. In: Graimann, Bernhard, Pfurtscheller, Gert, Allison, Brendan (eds.) Brain-Computer Interfaces, pp. 137–154. Springer, Berlin (2010)Google Scholar
  83. 83.
    Wang, J., Xu, G., Xie, J., Zhang, F., Li, L., Han, C., Sun, J.: Some highlights on EEG-based brain computer interface. Sciencepaper Online (2012). http://www.paper.edu.cn/en_releasepaper/content/4488562. Accessed 10 Nov 2013
  84. 84.
    Wester, M., Schultz, T.: Unspoken speech-speech recognition based on electroencephalography. Master’s thesis, Universität Karlsruhe (TH), Karlsruhe, Germany (2006)Google Scholar
  85. 85.
    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)CrossRefGoogle Scholar
  86. 86.
    Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain-computer interface research at the Wadsworth Center. IEEE Trans. Rehabil. Eng. 8(2), 222–226 (2000)CrossRefGoogle Scholar
  87. 87.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)Google Scholar
  88. 88.
    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. U S A 101(51), 17849–17854 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kusuma Mohanchandra
    • 1
  • Snehanshu Saha
    • 2
  • G. M. Lingaraju
    • 3
  1. 1.Department of Computer Science & EngineeringMedical Imaging Research Centre, Dayananda Sagar College of EngineeringBangaloreIndia
  2. 2.Department of Computer Science & Engineering and CBIMMCPESIT SouthBangaloreIndia
  3. 3.Department of Information Science & EngineeringM. S. Ramaiah Institute of TechnologyBangaloreIndia

Personalised recommendations