Skip to main content
SpringerLink
Log in

The Berlin Brain-Computer Interface

  • Chapter
Computational Intelligence: Research Frontiers (WCCI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5050))

Included in the following conference series:

Abstract

The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract subject-specific patterns from high-dimensional EEG-features optimized for revealing the user’s mental state. Classical BCI application are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([2] and see [3,4,5,6] for an overview on BCI). In these applications the BBCI uses natural motor competences of the users and specifically tailored pattern recognition algorithms for detecting the user’s intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [7] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Section 4.3 and 4.4.

This paper is a copy of the manuscript submitted to appear as [1].

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  1. Blankertz, B., Tangermann, M., Popescu, F., Krauledat, M., Fazli, S., Dónaczy, M., Curio, G., Müller, K.R.: The berlin brain-computer interface. In: Graimann, B., Pfurtscheller, G. (eds.) Non-Invasive and Invasive Brain-Computer Interfaces. Springer, Heidelberg (2008) The Frontiers Collection (in review, 2008)

    Google Scholar 

  2. Blankertz, B., Krauledat, M., Dornhege, G., Williamson, J., Murray-Smith, R., Müller, K.R.: A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 759–768. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  3. Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.): Toward Brain-Computer Interfacing. MIT Press, Cambridge (2007)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Allison, B., Wolpaw, E., Wolpaw, J.R.: Brain-computer interface systems: progress and prospects. Expert Rev. Med. Devices 4(4), 463–474 (2007)

    Article  Google Scholar 

  6. Pfurtscheller, G., Neuper, C., Birbaumer, N.: Human Brain-Computer Interface. In: Riehle, A., Vaadia, E. (eds.) Motor Cortex in Voluntary Movements, pp. 367–401. CRC Press, New York (2005)

    Google Scholar 

  7. Haynes, J., Sakai, K., Rees, G., Gilbert, S., Frith, C.: Reading hidden intentions in the human brain. Current Biology 17, 323–328 (2007)

    Article  Google Scholar 

  8. Blankertz, B., Dornhege, G., Lemm, S., Krauledat, M., Curio, G., Müller, K.R.: The Berlin Brain-Computer Interface: Machine learning based detection of user specific brain states. J. Universal Computer Sci. 12(6), 581–607 (2006)

    Google Scholar 

  9. Elbert, T., Rockstroh, B., Lutzenberger, W., Birbaumer, N.: Biofeedback of slow cortical potentials. I. Electroencephalogr Clin. Neurophysiol. 48, 293–301 (1980)

    Article  Google Scholar 

  10. Birbaumer, N., Kübler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., Flor, H.: The though translation device (TTD) for completly paralyzed patients. IEEE Trans. Rehab. Eng. 8(2), 190–193 (2000)

    Article  Google Scholar 

  11. Kornhuber, H.H., Deecke, L.: Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflügers Arch 284, 1–17 (1965)

    Article  Google Scholar 

  12. Lang, W., Lang, M., Uhl, F., Koska, C., Kornhuber, A., Deecke, L.: Negative cortical DC shifts preceding and accompanying simultaneous and sequential movements. Exp. Brain Res. 74(1), 99–104 (1989)

    Article  Google Scholar 

  13. Cui, R.Q., Huter, D., Lang, W., Deecke, L.: Neuroimage of voluntary movement: topography of the Bereitschaftspotential, a 64-channel DC current source density study. Neuroimage 9(1), 124–134 (1999)

    Article  Google Scholar 

  14. Blankertz, B., Dornhege, G., Krauledat, M., Müller, K.R., Kunzmann, V., Losch, F., Curio, G.: The Berlin Brain-Computer Interface: EEG-based communication without subject training. IEEE Trans. Neural Sys. Rehab. Eng. 14(2), 147–152 (2006)

    Article  Google Scholar 

  15. Blankertz, B., Dornhege, G., Krauledat, M., Kunzmann, V., Losch, F., Curio, G., Müller, K.R.: The berlin brain-computer interface: Machine-learning based detection of user specific brain states. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain-Computer Interfacing, pp. 85–101. MIT press, Cambridge (2007)

    Google Scholar 

  16. Blankertz, B., Dornhege, G., Schäfer, C., Krepki, R., Kohlmorgen, J., Müller, K.R., Kunzmann, V., Losch, F., Curio, G.: Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans. Neural Sys. Rehab. Eng. 11(2), 127–131 (2003)

    Article  Google Scholar 

  17. Krusienski, D., Schalk, G., McFarland, D.J., Wolpaw, J.: A mu-rhythm matched filter for continuous control of a brain-computer interface. IEEE Trans. Biomed. Eng. 54(2), 273–280 (2007)

    Article  Google Scholar 

  18. Nikulin, V.V., Brismar, T.: Phase synchronization between alpha and beta oscillations in the human electroencephalogram. Neuroscience 137, 647–657 (2006)

    Article  Google Scholar 

  19. Pfurtscheller, G., Lopes da Silva, F.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)

    Article  Google Scholar 

  20. Falkenstein, M., Hoormann, J., Christ, S., Hohnsbein, J.: ERP components on reaction errors and their functional significance: a tutorial. Biol. Psychol. 51(2-3), 87–107 (2000)

    Article  Google Scholar 

  21. Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L.: Effects of errors in choice reaction tasks on the ERP under focused and divided attention. In: Brunia, C., Gaillard, A., Kok, A. (eds.) Psychophysiological Brain Research, pp. 192–195. Tilburg University Press, Tilburg (1990)

    Google Scholar 

  22. Gehring, W., Coles, M., Meyer, D., Donchin, E.: The error-related negativity: an event-related brain potential accompanying errors. Psychophysiology 27, 34 (1993)

    Google Scholar 

  23. Vidal, F., Hasbroucq, T., Grapperon, J., Bonnet, M.: Is the ’error negativity’ specific to errors? Biological Psychology 51, 109–128 (2000)

    Article  Google Scholar 

  24. Miltner, W.H.R., Braun, C.H., Coles, M.G.H.: Event-related brain potentials following incorrect feedback in a time-estimation task: Evidence for a ‘generic’ neural system for error-detection. J. Cogn. Neurosci. 9, 788–798 (1997)

    Article  Google Scholar 

  25. Schie, H.T.v., Mars, R.B., Coles, M.G., Bekkering, H.: Modulation of activity in medial frontal and motor cortices during error observation. Nat. Neurosci. 7(5), 549–554 (2004)

    Article  Google Scholar 

  26. Ferrez, P., Millán, J.: Error-related eeg potentials generated during simulated brain-computer interaction. IEEE Trans. Biomed. Eng. (accepted)

    Google Scholar 

  27. Nunez, P.L., Srinivasan, R., Westdorp, A.F., Wijesinghe, R.S., Tucker, D.M., Silberstein, R.B., Cadusch, P.J.: EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin. Neurophysiol. 103(5), 499–515 (1997)

    Article  Google Scholar 

  28. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Proc. Magazine 25(1), 41–56 (2008)

    Article  Google Scholar 

  29. McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial filter selection for EEG-based communication. Electroencephalogr Clin. Neurophysiol. 103, 386–394 (1997)

    Article  Google Scholar 

  30. Hill, N., Lal, T.N., Tangermann, M., Hinterberger, T., Widman, G., Elger, C.E., Schölkopf, B., Birbaumer, N.: Classifying event-related desynchronization in EEG, ECoG and MEG signals. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain-Computer Interfacing, pp. 235–260. MIT press, Cambridge (2007)

    Google Scholar 

  31. Grosse-Wentrup, M., Gramann, K., Buss, M.: Adaptive spatial filters with predefined region of interest for EEG based brain-computer-interfaces. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 537–544 (2007)

    Google Scholar 

  32. Dornhege, G., Krauledat, M., Müller, K.R., Blankertz, B.: General signal processing and machine learning tools for BCI. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain-Computer Interfacing, pp. 207–233. MIT Press, Cambridge (2007)

    Google Scholar 

  33. Parra, L.C., Spence, C.D., Gerson, A.D., Sajda, P.: Recipes for the linear analysis of EEG. NeuroImage 28(2), 326–341 (2005)

    Article  Google Scholar 

  34. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)

    MATH  Google Scholar 

  35. Guger, C., Ramoser, H., Pfurtscheller, G.: Real-time EEG analysis with subject-specific spatial patterns for a Brain Computer Interface (BCI). IEEE Trans. Neural Sys. Rehab. Eng. 8(4), 447–456 (2000)

    Article  Google Scholar 

  36. Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehab. Eng. 8(4), 441–446 (2000)

    Article  Google Scholar 

  37. Lemm, S., Blankertz, B., Curio, G., Müller, K.R.: Spatio-spectral filters for improving classification of single trial EEG. IEEE Trans. Biomed. Eng. 52(9), 1541–1548 (2005)

    Article  Google Scholar 

  38. Dornhege, G., Blankertz, B., Krauledat, M., Losch, F., Curio, G., Müller, K.R.: Optimizing spatio-temporal filters for improving brain-computer interfacing. In: Advances in Neural Inf. Proc. Systems (NIPS 2005), vol. 18, pp. 315–322. MIT Press, Cambridge (2006)

    Google Scholar 

  39. Tomioka, R., Aihara, K., Müller, K.R.: Logistic regression for single trial EEG classification. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 1377–1384. MIT Press, Cambridge (2007)

    Google Scholar 

  40. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  41. Friedman, J.H.: Regularized discriminant analysis. J Amer. Statist. Assoc. 84(405), 165–175 (1989)

    Article  MathSciNet  Google Scholar 

  42. Neuper, C., Scherer, R., Reiner, M., Pfurtscheller, G.: Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res. Cogn. Brain Res. 25(3), 668–677 (2005)

    Article  Google Scholar 

  43. Nikulin, V.V., Hohlefeld, F.U., Jacobs, A.M., Curio, G.: Quasi-movements: A novel motor-cognitive phenomenon. In: Neuropsychologia (in press, 2008)

    Google Scholar 

  44. Blankertz, B., Dornhege, G., Krauledat, M., Müller, K.R., Curio, G.: The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage 37(2), 539–550 (2007)

    Article  Google Scholar 

  45. Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain-computer interface research at the Wadsworth Center. IEEE Trans. Rehab. Eng. 8(2), 222–226 (2000)

    Article  Google Scholar 

  46. Schlögl, A., Kronegg, J., Huggins, J., Mason, S.G.: Evaluation Criteria for BCI Research. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Towards Brain-Computer Interfacing, pp. 297–312. MIT press, Cambridge (2007)

    Google Scholar 

  47. Dornhege, G.: Increasing Information Transfer Rates for Brain-Computer Interfacing. PhD thesis, University of Potsdam (2006)

    Google Scholar 

  48. Müller, K.R., Blankertz, B.: Toward noninvasive brain-computer interfaces. IEEE Signal Proc. Magazine 23(5), 125–128 (2006)

    Google Scholar 

  49. Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., Müller, K.R.: The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects. IEEE Trans. Biomed. Eng. (accepted, 2008)

    Google Scholar 

  50. Kübler, A., Müller, K.R.: An introduction to brain computer interfacing. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain-Computer Interfacing, pp. 1–25. MIT press, Cambridge (2007)

    Google Scholar 

  51. Schalk, G., Wolpaw, J.R., McFarland, D.J., Pfurtscheller, G.: EEG-based communication: presence of an error potential. Clin. Neurophysiol 111, 2138–2144 (2000)

    Article  Google Scholar 

  52. Blankertz, B., Schäfer, C., Dornhege, G., Curio, G.: Single Trial Detection of EEG Error Potentials: A Tool for Increasing BCI Transmission Rates. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 1137–1143. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  53. Parra, L., Spence, C., Gerson, A., Sajda, P.: Response error correction - a demonstration of improved human-machine performance using real-time EEG monitoring. IEEE Trans. Neural. Sys. Rehab. Eng. 11(2), 173–177 (2003)

    Article  Google Scholar 

  54. Ferrez, P., Millán, J.: You are wrong! – automatic detection of interaction errors from brain waves. In: 19th International Joint Conference on Artificial Intelligence, pp. 1413–1418 (2005)

    Google Scholar 

  55. Kohlmorgen, J., Dornhege, G., Braun, M., Blankertz, B., Müller, K.R., Curio, G., Hagemann, K., Bruns, A., Schrauf, M., Kincses, W.: Improving human performance in a real operating environment through real-time mental workload detection. In: Dornhege, G., del, R., Millán, J., Hinterberger, T., McFarland, D., Müller, K.R. (eds.) Toward Brain-Computer Interfacing, pp. 409–422. MIT press, Cambridge (2007)

    Google Scholar 

  56. 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)

    Article  Google Scholar 

  57. Popescu, F., Fazli, S., Badower, Y., Blankertz, B., Müller, K.R.: Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS ONE 2(7) (2007)

    Google Scholar 

  58. Fazli, S., Dónaczy, M., Kawanabe, M., Popescu, F.: Asynchronous, adaptive BCI using movement imagination training and rest-state inference. In: IASTED’s Proceedings on Artificial Intelligence and Applications, pp. 85–90 (2008)

    Google Scholar 

  59. Krauledat, M., Dornhege, G., Blankertz, B., Curio, G., Müller, K.R.: The Berlin brain-computer interface for rapid response. Biomed. Tech. 49(1), 61–62 (2004)

    Google Scholar 

  60. Kübler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J., Birbaumer, N.: Brain-computer communication: Unlocking the locked in. Psychol. Bull 127(3), 358–375 (2001)

    Article  Google Scholar 

  61. 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. Neurology 64(10), 1775–1777 (2005)

    Article  Google Scholar 

  62. Birbaumer, N., Cohen, L.: Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621–636 (2007)

    Article  Google Scholar 

  63. Birbaumer, N., Weber, C., Neuper, C., Buch, E., Haapen, K., Cohen, L.: Physiological regulation of thinking: brain-computer interface (BCI) research. Prog. Brain Res. 159, 369–391 (2006)

    Article  Google Scholar 

  64. Hochberg, L., Serruya, M., Friehs, G., Mukand, J., Saleh, M., Caplan, A., Branner, A., Chen, D., Penn, R., Donoghue, J.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006)

    Article  Google Scholar 

  65. 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. Journal of Multimedia Tools and Applications 33(1), 73–90 (2007)

    Article  Google Scholar 

  66. Krepki, R., Curio, G., Blankertz, B., Müller, K.R.: Berlin brain-computer interface - the hci communication channel for discovery. Int. J. Hum. Comp. Studies 65, 460–477 (2007) Special Issue on Ambient Intelligence

    Article  Google Scholar 

  67. Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Sys. Rehab. Eng. 15(4), 473–482 (2007)

    Article  Google Scholar 

  68. Gerson, A., Parra, L., Sajda, P.: Cortically coupled computer vision for rapid image search. IEEE Trans. Neural. Sys. Rehab. Eng. 14(2), 174–179 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany

    Benjamin Blankertz, Michael Tangermann, Matthias Krauledat & Klaus-Robert Müller

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Benjamin Blankertz, Florin Popescu, Siamac Fazli, Márton Dónaczy & Klaus-Robert Müller

  3. Campus Benjamin Franklin, Charité University Medicine Berlin, Germany

    Gabriel Curio

Authors
  1. Benjamin Blankertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Tangermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florin Popescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthias Krauledat
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Siamac Fazli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Márton Dónaczy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gabriel Curio
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Klaus-Robert Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Jacek M. Zurada Gary G. Yen Jun Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Blankertz, B. et al. (2008). The Berlin Brain-Computer Interface. In: Zurada, J.M., Yen, G.G., Wang, J. (eds) Computational Intelligence: Research Frontiers. WCCI 2008. Lecture Notes in Computer Science, vol 5050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68860-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-68860-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68858-7

  • Online ISBN: 978-3-540-68860-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation