Skip to main content

Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices

  • Chapter
Robust Intelligent Systems

Abstract

A brain-computer interface (BCI) transforms brain activity into commands that can control computers and other technologies. Because brain signals recorded non-invasively from the scalp are difficult to interpret, robust signal processing methods have to be applied. Although state-of-the-art signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very small compared with conventional human interaction interfaces. Therefore, BCI applications have to compensate for the unreliability and low information content of the BCI output. Controlling a wheelchair or a robotic arm would be slow, frustrating, or even dangerous if it solely relied on BCI output. Intelligent devices, however, such as a wheelchair that can automatically avoid collisions and dangerous situations or a service robot that can autonomously conduct goal-directed tasks and independently detect and resolve safety issues, are much more suitable for being controlled by an “unreliable” control signal like that provided by a BCI.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Allison, B., Graimann, B., and Gräser, A. (2007a). Why use a BCI if you are healthy? In Proceedings of the International Conference on Advances in Computer Entertainment, pages 7–11, Salzburg, Austria, 13–15 June.

    Google Scholar 

  • Allison, B. Z. and Pineda, J. A. (2003). ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):110–113.

    Article  Google Scholar 

  • Allison, B. Z. and Pineda, J. A. (2006). Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. International Journal of Psychophysiology, 59(2):127–140.

    Article  Google Scholar 

  • Allison, B. Z., Wolpaw, E. W., and Wolpaw, A. R. (2007b). Brain-computer interface systems: progress and prospects. Expert Review of Medical Devices, 4(4):463–474.

    Article  Google Scholar 

  • Birbaumer, N. and Cohen, L. G. (2007). Brain-computer interfaces: communication and restoration of movement in paralysis. Journal of Physiology-London, 579(3):621–636.

    Article  Google Scholar 

  • Blankertz, B., Dornhege, G., Krauledat, M., Müller, K. R., and Curio, G. (2007). The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage, 37(2):539–550.

    Article  Google Scholar 

  • Cook, A. and Hussey, S. (2002). Assistive Technologies: Principles and Practice. Mosby, St. Louis, 2nd edition.

    Google Scholar 

  • Donchin, E., Spencer, K. M., and Wijesinghe, R. (2000). The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Transactions on Rehabilitation Engineering, 8(2):174–179.

    Article  Google Scholar 

  • Dornhege, G., Blankertz, B., Curio, G., and Müller, K. R. (2004). Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Transactions on Biomedical Engineering, 51(6):993–1002.

    Article  Google Scholar 

  • Dornhege, G., Millan, J., Hinterberger, T., McFarland, D. J., and Müller, K. R., editors (2007). Toward Brain-Computer Interfacing. MIT Press, Cambridge, MA.

    Google Scholar 

  • Farwell, L. and Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6):510–523.

    Article  Google Scholar 

  • Fonseca, C., Cunha, J., and Martins, R. (2007). A novel dry active electrode for EEG recording. IEEE Transactions on Biomedical Engineering, 54:162–165.

    Article  Google Scholar 

  • Friman, O., Lüth, T., Volosyak, I., and Gröser, A. (2007a). Spelling with steady-state visual evoked potentials. In Proceedings of the 3rd International IEEE/EMBS Conference on Neural Engineering (CNE’07), pages 510–523, Hawaii, 2–5 May 2007.

    Google Scholar 

  • Friman, O., Volosyak, I., and Gröser, A. (2007b). Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Transactions on Biomedical Engineering, 54(4):742–750.

    Article  Google Scholar 

  • Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Computer Science and Scientific Computing. Academic Press, Boston, 2nd edition.

    Google Scholar 

  • Gao, X., Xu, D., Cheng, M., and Gao, S. (2003). A BCI-based environmental controller for the motion-disabled. IEEE Transactions on Neural System and Rehabilitation Engineering, 11(2):137–140.

    Article  Google Scholar 

  • Graimann, B. (2006). Event-related (de)synchronization in bioelectrical brain signals and its use in brain-computer communication. PhD thesis, Habilitationsschrift: Graz University of Technology.

    Google Scholar 

  • Graimann, B., Allison, B., and Gröser, A. (2007). New applications for non-invasive brain-computer interfaces and the need for engaging training environments. In Proceedings of the International Conference on Advances in Computer Entertainment, pages 25–28, Salzburg, Austria, 13–15 June.

    Google Scholar 

  • Graimann, B., Huggins, J. E., Levine, S. P., and Pfurtscheller, G. (2004). Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis. IEEE Transactions on Biomedical Engineering, 51(6):954–962.

    Article  Google Scholar 

  • Graimann, B. and Pfurtscheller, G. (2006). Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain. In Neuper, C. and Klimesch, W., editors, Event-related Dynamics of Brain Oscillations., Progress in Brain Research, pages 79–97. Elsevier, Amsterdam.

    Google Scholar 

  • Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., and Pfurtscheller, G. (2003). How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):145–147.

    Article  Google Scholar 

  • Hill, J., Lal, T., Tangermann, M., Hinterberger, T., Widman, G., and Elger, C. (2007). Classifying Event-Related Desynchronization in EEG, ECoG, and MEG Signals. In Dornhege, G.,Millan, J., Hinterberger, T., McFarland, D. J., and Müller, K. R., editors, Toward Brain-Computer Interfacing, pages 235–259. MIT Press, Cambridge, MA.

    Google Scholar 

  • Hillman, M. (2003). Rehabilitation robotics from past to present–a historical perspective. In Proceedings of the 8th International Conference on Rehabilitation Robotics (ICORR’03), pages 101–105, Daelon, Korea.

    Google Scholar 

  • Ivlev, O., Martens, C., and Gröser, A. (2005). Rehabilitation robots FRIEND-I and FRIEND-II with the dexterous lightweight manipulator. In Prcoceedings of the 3rd International Congress on Restoration of (Wheeled) Mobility in SCI Rehabilitation, volume 5, pages 111–123, Amsterdam, The Netherlands, 19–21 April.

    Google Scholar 

  • Kleber, B. and Birbaumer, N. (2005). Direct brain communication: neuroelectric and metabolic approaches at Tübingen. Cognitive Processing, 6:65–74.

    Article  Google Scholar 

  • Krieg-Brückner, B., Frese, U., Lüttich, K., Mandel, C., Mossakowski, T., and Ross, R. (2005). Specification of an ontology for route graphs. In Freska, C., Knauff, M., Krieg-Brückner, B., Nebel, B., and Barkowsky, T., editors, Spatial Cognition IV, volume 3343 of Lecture Notes in Artificial Intelligence, pages 390–412. Springer, Berlin, Heidelberg.

    Google Scholar 

  • Lemm, S., Blankertz, B., Curio, G., and Müller, K. R. (2005). Spatio-spectral filters for improving the classification of single trial EEG. IEEE Transactions on Biomedical Engineering, 52(9):1541–1548.

    Article  Google Scholar 

  • Levine, S. P., Bell, D. A., Jaros, L. A., Simpson, R. C., Koren, Y., and Borenstein, J. (1999). The NavChair assistive wheelchair navigation system. IEEE Transactions on Rehabilitation Engineering, 7(4):443–451.

    Article  Google Scholar 

  • Lüth, T., Ojdanic, D., Friman, O., Prenzel, O., and Gröser, A. (2007). Low-level control in a semi-autonmous rehabilitation robotic system via a brain-computer interface. In Proceedings of the 10th International Conference on Rehabilitation Robotics (ICORR’07), pages 721–728, Noordwijk, Netherlands, 13–15 June.

    Google Scholar 

  • Mandel, C. and Frese, U. (2007). Comparison of wheelchair user interfaces for the paralysed: head-joystick vs. verbal path selection from an offered route-set. In Proceedings of the 3rd European Conference on Mobile Robots (ECMR’07), pages 217–222, Freiburg, Germany, 19–21 September.

    Google Scholar 

  • Mandel, C., Frese, U., and Roefer, T. (2006). Robot navigation based on the mapping of coarse qualitative route descriptions to route graphs. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’06), pages 205–210, Beijing, China, 9–13 October.

    Google Scholar 

  • Marr, D. (1982). Vision: a computational investigation into the human representation and processing of visual information. W.H. Freeman, San Francisco.

    Google Scholar 

  • McFarland, D. J., McCane, L. M., David, S. V., and Wolpaw, J. R. (1997). Spatial filter selection for EEG-based communication. Electroencephalography and Clinical Neurophysiology, 103(3):386–394.

    Article  Google Scholar 

  • McFarland, D. J., Sarnacki, W. A., and Wolpaw, J. R. (2003). Brain-computer interface (BCI) operation: optimizing information transfer rates. Biological Psychology, 63(3):237–251.

    Article  Google Scholar 

  • Middendorf, M., McMillan, G., Calhoun, G., and Jones, K. S. (2000). Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Transactions on Rehabilitation Engineering, 8(2):211–214.

    Article  Google Scholar 

  • Millan, J., Renkens, F., Mourino, J., and Gerstner, W. (2004). Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Transactions on Biomedical Engineering, 51(6):1026–1033.

    Article  Google Scholar 

  • Müller, G. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J., and Pfurtscheller, G. (2003a). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neuroscience Letters, 340(2):143–147.

    Article  Google Scholar 

  • Müller, K., Krauledat, M., Dornhege, G., Curio, G., and Blankertz, B. (2004). Machine learning techniques for brain-computer interfaces. Biomed Tech, 49(1):11–22.

    Article  Google Scholar 

  • Müller, K. R., Anderson, C. W., and Birch, G. E. (2003b). Linear and nonlinear methods for brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2):165–169.

    Article  Google Scholar 

  • Müller-Putz, G. R., Scherer, R., Pfurtscheller, G., and Rupp, R. (2005). EEG-based neuroprosthesis control: a step towards clinical practice. Neuroscience Letters, 382(1–2):169–174.

    Article  Google Scholar 

  • Naeem, M., Brunner, C., Leeb, R., Graimann, B., and Pfurtscheller, G. (2006). Seperability of four-class motor imagery data using independent components analysis. Journal of Neural Engineering, 3(3):208–216.

    Article  Google Scholar 

  • Pfurtscheller, G., Graimann, B., and Neuper, C. (2006a). EEG-based Brain-Computer Interface Systems and Signal Processing. In Akay, M., editor, Encyclopedia of Biomedical Engineering, volume 2, pages 1156–1166. John Wiley & Sons, Hoboken, NJ.

    Google Scholar 

  • Pfurtscheller, G., Müzsller-Putz, G. R., Schlogl, A., Graimann, B., Scherer, R., Leeb, R., Brunner, C., Keinrath, C., Lee, F., Townsend, G., Vidaurre, C., and Neuper, C. (2006b). 15 years of BCI research at Graz University of Technology: current projects. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):205–210.

    Article  Google Scholar 

  • Prenzel, O. (2005). Semi-autonomous object anchoring for service-robots. Methods and Applications in Automation, 1:57–68.

    Google Scholar 

  • Prenzel, O., Martens, C., Cyriacks, M., Wang, C., and Gröser, A. (2007). System-controlled user interaction within the service robotic control architecture MASSiVE. Robotica, 25(2):237–244.

    Article  Google Scholar 

  • Ramoser, H., Müller-Gerking, J., and Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4):441–446.

    Article  Google Scholar 

  • Sellers, E. W. and Donchin, E. (2006). A P300-based brain-computer interface: initial tests by ALS patients. Clinical Neurophysiology, 117(3):538–548.

    Article  Google Scholar 

  • Sellers, E. W., Krusienski, D. J., McFarland, D. J., Vaughan, T. M., and Wolpaw, J. R. (2006). A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biological Psychology, 73(3):242–252.

    Article  Google Scholar 

  • Simpson, R. (2005). Smart wheelchairs: a literature review. Journal of Rehabilitation Research and Development, 42(4):423–436.

    Article  Google Scholar 

  • Valbuena, D., Cyriacks, M., Friman, O., Volosyak, I., and Gröser, A. (2007). Brain-computer interface for high-level control of rehabilitation robotic systems. In Proceedings of the 10th International Conference on Rehabilitation Robotics (ICORR’07), pages 619–625, Noordwijk, Netherlands, 13–15 June. IEEE Press.

    Google Scholar 

  • Vidal, J. J. (1973). Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 2:157–180.

    Article  Google Scholar 

  • Wolpaw, J. R. (2007). Brain-computer interfaces as new brain output pathways. Journal ofPhysiology, 579(Pt 3):613–619.

    Article  Google Scholar 

  • Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., and Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6):767–791.

    Article  Google Scholar 

  • Wolpaw, J. R., McFarland, D. J., Neat, G. W., and Forneris, C. A. (1991). An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology, 78(3):252–259.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bernhard Graimann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag London Limited

About this chapter

Cite this chapter

Graimann, B., Allison, B., Mandel, C., Lüth, T., Valbuena, D., Gräser, A. (2008). Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices. In: Schuster, A. (eds) Robust Intelligent Systems. Springer, London. https://doi.org/10.1007/978-1-84800-261-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-84800-261-6_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-260-9

  • Online ISBN: 978-1-84800-261-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics