Advertisement

Brain–Machine Interfaces for Persons with Disabilities

  • Kenji Kansaku

Abstract

The brain–machine interface (BMI) or brain–computer interface (BCI) is a new interface technology that utilizes neurophysiological signals from the brain to control external machines or computers. We used electroencephalography signals in a BMI system that enables environmental control and communication using the P300 paradigm, which presents a selection of icons arranged in a matrix. The subject focuses attention on one of the flickering icons in the matrix as a target. We also prepared a green/blue flicker matrix because this color combination is considered the safest chromatic combination for patients with photosensitive epilepsy. We showed that the green/blue flicker matrix was associated with a better subjective feeling of comfort than was the white/gray flicker matrix, and we also found that the green/blue flicker matrix was associated with better performance. We further added augmented reality (AR) to make an AR-BMI system, in which the user’s brain signals controlled an agent robot and operated devices in the robot’s environment. Thus, the user’s thoughts became reality through the robot’s eyes, enabling the augmentation of real environments outside of the human body. Studies along these lines may provide ­useful information to expand the range of activities in persons with disabilities.

Keywords

Augmented Reality Motor Imagery Task Agent Robot Offline Evaluation P300 Speller 
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

Acknowledgements

These studies were conducted with the NRCD post-doctoral fellows Drs. Kouji Takano, Tomoaki Komatsu, Shiro Ikegami, and Naoki Hata. I thank Dr. Günter Edlinger for his help, and Drs. Yasoichi Nakajima and Motoi Suwa for their continuous encouragement.

References

  1. 1.
    Lebedev MA, Nicolelis MA (2006) Brain–machine interfaces: past, present and future. Trends Neurosci 29:536–546PubMedCrossRefGoogle Scholar
  2. 2.
    Birbaumer N, Cohen LG (2007) Brain–computer interfaces: communication and restoration of movement in paralysis. J Physiol 579:621–636PubMedCrossRefGoogle Scholar
  3. 3.
    Hochberg L, Serruya M, Friehs G, Mukand J, Saleh M, Caplan AH, Branner A, Chen D, Penn R, Donoghue J (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442:164–171PubMedCrossRefGoogle Scholar
  4. 4.
    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 105:19486–19491PubMedCrossRefGoogle Scholar
  5. 5.
    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 107:4430–4435PubMedCrossRefGoogle Scholar
  6. 6.
    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 101:17849–17854PubMedCrossRefGoogle Scholar
  7. 7.
    Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G (2003) How many people are able to operate an EEG-based brain–computer interface (BCI)? IEEE Trans Neural Syst Rehabil Eng 11:145–147PubMedCrossRefGoogle Scholar
  8. 8.
    Bai O, Mari Z, Vorbach S, Hallett M (2005) Asymmetric spatiotemporal patterns of ­event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study. Clin Neurophysiol 116:1213–1221PubMedCrossRefGoogle Scholar
  9. 9.
    Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31:153–159PubMedCrossRefGoogle Scholar
  10. 10.
    Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–523PubMedCrossRefGoogle Scholar
  11. 11.
    Piccione F, Giorgi F, Tonin P, Priftis K, Giove S, Silvoni S, Palmas G, Beverina F (2006) P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol 117:531–537PubMedCrossRefGoogle Scholar
  12. 12.
    Sellers EW, Donchin E (2006) A P300-based brain–computer interface: initial tests by ALS patients. Clin Neurophysiol 117:538–548PubMedCrossRefGoogle Scholar
  13. 13.
    Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Halder S, Mochty U, Krusienski DJ, Vaughan TM, Wolpaw JR, Birbaumer N, Kubler A (2008) A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119:1909–1916PubMedCrossRefGoogle Scholar
  14. 14.
    Komatsu T, Hata N, Nakajima Y, Kansaku K (2008) A non-training EEG-based BMI system for environmental control. Neurosci Res Suppl 61:S251Google Scholar
  15. 15.
    Ikegami S, Takano K, Komatsu T, Saeki N, Kansaku K (2009) Operation of a BMI based environmental control system by patients with cervical spinal cord injury. Neuroscience meeting planner. Society for Neuroscience, Chicago. Online Program No. 664.16Google Scholar
  16. 16.
    Takano K, Ikegami S, Komatsu T, Kansaku K (2009) Green/blue flicker matrices for the P300 BCI improve the subjective feeling of comfort. Neurosci Res Suppl 65:S182CrossRefGoogle Scholar
  17. 17.
    Takano K, Komatsu T, Hata N, Nakajima Y, Kansaku K (2009) Visual stimuli for the P300 brain–computer interface: a comparison of white/gray and green/blue flicker matrices. Clin Neurophysiol 120:1562–1566PubMedCrossRefGoogle Scholar
  18. 18.
    Kansaku K, Hata N, Takano K (2010) My thoughts through a robot’s eyes: an augmented reality–brain machine interface. Neurosci Res 66:219–222PubMedCrossRefGoogle Scholar
  19. 19.
    Takano K, Hata N, Nakajima Y, Kansaku K (2010) AR-BMI operated with a HMD: effects of channel selection. Neuroscience meeting planner. Society for Neuroscience, San Diego. Online Program No. 295.18Google Scholar
  20. 20.
    Kaper M, Meinicke P, Grossekathoefer U, Lingner T, Ritter H (2004) BCI Competition 2003–Data set IIb: support vector machines for the P300 speller paradigm. IEEE Trans Biomed Eng 51:1073–1076PubMedCrossRefGoogle Scholar
  21. 21.
    Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR (2006) A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol Psychol 73:242–252PubMedCrossRefGoogle Scholar
  22. 22.
    Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR (2008) Toward enhanced P300 speller performance. J Neurosci Methods 167:15–21PubMedCrossRefGoogle Scholar
  23. 23.
    Parra J, Lopes da Silva FH, Stroink H, Kalitzin S (2007) Is colour modulation an independent factor in human visual photosensitivity? Brain 130:1679–1689PubMedCrossRefGoogle Scholar
  24. 24.
    Krusienski DJ, Sellers EW, Vaughan TM (2007) Common spatio-temporal patterns for the p300 speller. In: 3rd International IEEE EMBS conference on neural engineering, Kohala Coast, Hawaii, USA, pp 421–424Google Scholar
  25. 25.
    Lu S, Guan C, Zhang H (2008) Unsupervised brain computer interface based on inter-subject information. In: 30th Annual international IEEE EMBS conference, Vancouver, British Columbia, Canada, pp 638–641Google Scholar
  26. 26.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791PubMedCrossRefGoogle Scholar
  27. 27.
    Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: International workshop on augmented reality, San Francisco, USA, pp 85–94Google Scholar
  28. 28.
    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 49:1181–1186PubMedCrossRefGoogle Scholar
  29. 29.
    Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain–computer interface. IEEE Trans Rehabil Eng 8:174–179PubMedCrossRefGoogle Scholar
  30. 30.
    Bostanov V (2004) BCI Competition 2003 – data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans Biomed Eng 51:1057–1061PubMedCrossRefGoogle Scholar
  31. 31.
    Townsend G, LaPallo BK, Boulay CB, Krusienski DJ, Frye GE, Hauser CK, Schwartz NE, Vaughan TM, Wolpaw JR, Sellers EW (2010) A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121:1109–1120PubMedCrossRefGoogle Scholar
  32. 32.
    Komatsu T, Takano K, Nakajima Y, Kansaku K (2009) A BMI based environmental control system: a combination of sensorimotor rhythm, P300, and virtual reality. Neuroscience meeting planner. Society for Neuroscience, Chicago. Online Program No. 360.14Google Scholar
  33. 33.
    Komatsu T, Takano K, Ikegami S, Kansaku K (2010) A development of a BCI-based OT-assist suit for paralyzed upper extremities. Neuroscience meeting planner. Society for Neuroscience, San Diego. Online Program No. 295.9Google Scholar

Copyright information

© Springer 2011

Authors and Affiliations

  1. 1.Systems Neuroscience Section, Department of Rehabilitation for Brain FunctionsResearch Institute of National Rehabilitation Center for Persons with Disabilities (NRCD)TokorozawaJapan

Personalised recommendations