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Rethinking BCI Paradigm and Machine Learning Algorithm as a Symbiosis: Zero Calibration, Guaranteed Convergence and High Decoding Performance

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Brain-Computer Interface Research

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

Abstract

In the past, the decoding quality of brain-computer interface (BCI) systems was often enhanced by independently improving either the machine learning algorithms or the BCI paradigms. We propose to take a novel perspective instead by optimizing the whole system, paradigm and decoder, jointly. To exemplify this holistic idea, we introduce learning from label proportions (LLP) as a new classification approach and prove its value for visual event-related potential (ERP) signals of the EEG. LLP utilizes the existence of subgroups with different label proportions in the data. This leads to a conceptually simple BCI system which combines previously unseen capabilities: (1) it does not require calibration and learns from unlabeled data, (2) under i.i.d. conditions, LLP is guaranteed to obtain the optimal decoder for online data, (3) under violation of stationarity assumptions, LLP can continuously adapt to the changing data, and (4) it can, in practice, replace a traditional supervised decoder when combined with an expectation-maximization algorithm.

David Hübner, Pieter-Jan Kindermans—These authors contributed equally.

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References

  1. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  2. P. Shenoy, M. Krauledat, B. Blankertz, R.P.N. Rao, K.-R. Müller, Towards adaptive classification for BCI. J. Neural Eng. 3(1), R13 (2006)

    Article  Google Scholar 

  3. S.J. Luck, An Introduction to the Event-Related Potential Technique (MIT Press, 2014)

    Google Scholar 

  4. L.A. Farwell, E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)

    Article  Google Scholar 

  5. M. Tangermann et al., Optimized stimulation events for a visual ERP BCI. Int. J. Bioelectromagnetism 13(3), 119–120 (2011)

    Google Scholar 

  6. M. Tangermann et al., Data driven neuroergonomic optimization of BCI stimuli, in Proceedings of 5th International BCI Conference Graz (2011),  pp. 160–163

    Google Scholar 

  7. T. Kaufmann, S.M. Schulz, C. Grünzinger, A. Kübler, Flashing characters with famous faces improves ERP-based brain–computer interface performance. J. Neural Eng. 8(5), 056016 (2011)

    Article  Google Scholar 

  8. B. Hong, F. Guo, T. Liu, X. Gao, S. Gao, N200-speller using motion-onset visual response. Clin. Neurophysiol. 120(9), 1658–1666 (2009)

    Article  Google Scholar 

  9. G. Townsend et al., A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin. Neurophysiol. 121(7), 1109–1120 (2010)

    Article  Google Scholar 

  10. T. Verhoeven, P. Buteneers, J.R. Wiersema, J. Dambre, P.J. Kindermans, Towards a symbiotic brain–computer interface: exploring the application–decoder interaction. J. Neural Eng. 12(6), 066027 (2015)

    Article  Google Scholar 

  11. G. Bin, X. Gao, Y. Wang, Y. Li, B. Hong, S. Gao, A high-speed BCI based on code modulation VEP. J. Neural Eng. 8(2), 025015 (2011)

    Article  Google Scholar 

  12. X. Chen, Y. Wang, M. Nakanishi, X. Gao, T.-P. Jung, S. Gao, High-speed spelling with a noninvasive brain–computer interface. Proc. Natl. Acad. Sci. 112(44), E6058–E6067 (2015)

    Article  Google Scholar 

  13. M. Schreuder, B. Blankertz, M. Tangermann, A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE 5(4), e9813 (2010)

    Article  Google Scholar 

  14. M. Schreuder, T. Rost, M. Tangermann, Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Front. Neurosci. 5, 112 (2011)

    Article  Google Scholar 

  15. S. Gao, Y. Wang, X. Gao, B. Hong, Visual and auditory brain–computer interfaces. IEEE Trans. Biomed. Eng. 61(5), 1436–1447 (2014)

    Article  Google Scholar 

  16. G. Pfurtscheller, C. Neuper, Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)

    Article  Google Scholar 

  17. A. Kübler, N. Neumann, J. Kaiser, B. Kotchoubey, T. Hinterberger, N.P. Birbaumer, Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 82(11), 1533–1539 (2001)

    Article  Google Scholar 

  18. K.-R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, B. Blankertz, 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 

  19. B. Blankertz, S. Lemm, M. Treder, S. Haufe, K.-R. Müller, Single-trial analysis and classification of ERP components, a tutorial. Neuroimage 56(2), 814–825 (2011)

    Article  Google Scholar 

  20. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi, A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), R1 (2007)

    Article  Google Scholar 

  21. B. Blankertz, G. Curio, K.-R. Müller, Classifying single trial EEG: towards brain computer interfacing, in Advances in Neural Information Processing Systems, ed. by T.G. Dietterich, S. Becker, Z. Ghahramani, vol. 14 (MIT Press, 2002), pp. 157–164

    Google Scholar 

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

    Article  Google Scholar 

  23. M. Krauledat, M. Tangermann, B. Blankertz, K.-R. Müller, Towards zero training for brain-computer interfacing. PLoS ONE 3(8), e2967 (2008)

    Article  Google Scholar 

  24. S. Fazli, F. Popescu, M. Danóczy, B. Blankertz, K.-R. Müller, C. Grozea, Subject-independent mental state classification in single trials. Neural Netw. 22(9), 1305–1312 (2009)

    Article  Google Scholar 

  25. S. Fazli, S. Dähne, W. Samek, F. Bießmann, K.-R. Müller, Learning from more than one data source: data fusion techniques for sensorimotor rhythm-based brain-computer interfaces. Proc. IEEE 103(6), 891–906 (2015)

    Article  Google Scholar 

  26. V. Jayaram, M. Alamgir, Y. Altun, B. Schölkopf, M. Grosse-Wentrup, Transfer learning in brain-computer interfaces. IEEE Comput. Intell. Mag. 11(1), 20–31 (2016)

    Article  Google Scholar 

  27. P.-J. Kindermans, M. Tangermann, K.-R. Müller, B. Schrauwen, Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller. J. Neural Eng. 11(3), 035005 (2014)

    Article  Google Scholar 

  28. C. Vidaurre, M. Kawanabe, P. von Bünau, B. Blankertz, K.-R. Müller, Toward unsupervised adaptation of LDA for brain–computer interfaces. IEEE Trans. Biomed. Eng. 58(3), 587–597 (2011)

    Article  Google Scholar 

  29. P.-J. Kindermans, D. Verstraeten, B. Schrauwen, A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI. PLoS ONE 7(4), e33758 (2012)

    Article  Google Scholar 

  30. P.-J. Kindermans, M. Schreuder, B. Schrauwen, K.-R. Müller, M. Tangermann, True zero-training brain-computer interfacing—an online study. PLoS ONE 9(7), e102504 (2014)

    Article  Google Scholar 

  31. R. Chavarriaga, P.W. Ferrez, J. del R. Millán, To err is human: learning from error potentials in brain-computer interfaces, in Proceedings of the International Conference on Cognitive Neurodynamics (ICCN) (2007), pp. 777–782

    Google Scholar 

  32. I. Iturrate, R. Chavarriaga, L. Montesano, J. Minguez, J. del R. Millán, Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Sci. Rep. 5(13893), 1–10 (2015)

    Google Scholar 

  33. T.O. Zander, L.R. Krol, N.P. Birbaumer, K. Gramann, Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity. Proc. Natl. Acad. Sci. 113(52), 14898–14903 (2016)

    Article  Google Scholar 

  34. F. Lotte, C. Jeunet, Towards improved BCI based on human learning principles, in The 3rd International Winter Conference on Brain-Computer Interface (2015), pp. 1–4

    Google Scholar 

  35. L.F. Nicolas-Alonso, J. Gomez-Gil, Brain computer interfaces, a review. Sensors 12(2), 1211–1279 (2012)

    Article  Google Scholar 

  36. J.d.R. Millán et al., Combining brain–computer interfaces and assistive technologies: State-of-the-art and challenges. Front. Neurosci. 4(161), 1–15 (2010)

    Google Scholar 

  37. D. Hübner, T. Verhoeven, K. Schmid, K.-R. Müller, M. Tangermann, P.-J. Kindermans, Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PLoS ONE 12(4), e0175856 (2017)

    Article  Google Scholar 

  38. N. Quadrianto, A.J. Smola, T.S. Caetano, Q.V. Le, Estimating labels from label proportions. J. Mach. Learn. Res. 10, 2349–2374 (2009)

    MathSciNet  MATH  Google Scholar 

  39. T. Verhoeven, D. Hübner, M. Tangermann, K.-R. Müller, J. Dambre, P.-J. Kindermans, Improving zero-training brain-computer interfaces by mixing model estimators. J. Neural Eng. 14(3), 036021 (2017)

    Article  Google Scholar 

  40. D. Hübner, T. Verhoeven, P.-J. Kindermans, M. Tangermann, Mixing two unsupervised estimators for event-related potential decoding: an online evaluation, in Proceedings of the 7th International Brain-Computer Interface Meeting 2017: From Vision to Reality (2017), pp. 198–203

    Google Scholar 

  41. K.-R. Müller, B. Blankertz, Toward noninvasive brain-computer interfaces. IEEE Signal Process. Mag. 23(5), 128–126 (2006)

    Article  Google Scholar 

  42. D. Hübner, P.-J. Kindermans, T. Verhoeven, M. Tangermann, Improving learning from label proportions by reducing the feature dimensionality, in Proceedings of the 7th International Brain-Computer Interface Meeting 2017: From Vision to Reality (2017), pp. 186–191

    Google Scholar 

  43. M. Schreuder, J. Höhne, B. Blankertz, S. Haufe, T. Dickhaus, M. Tangermann, Optimizing event-related potential based brain–computer interfaces: a systematic evaluation of dynamic stopping methods. J. Neural Eng. 10(3), 036025 (2013)

    Article  Google Scholar 

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Acknowledgements

DH and MT thankfully acknowledge the support by BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086. DH and MT further acknowledge the bwHPC initiative, grant INST 39/963-1 FUGG. PJK gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement NO. 657679. TV thankfully acknowledges financial support from the Special Research Fund of Ghent University. KRM thanks DFG (DFG SPP 1527, MU 987/14-1) and the Federal Ministry for Education and Research (BMBF No. 2017-0-00451) as well as support by the Brain Korea 21 Plus Program by the Institute for Information and Communications Technology Promotion (IITP) grant (1IS14013A) funded by the Korean government.

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Authors and Affiliations

  1. Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau, Germany

    David Hübner & Michael Tangermann

  2. Berlin Institute of Technology, Berlin, Germany

    Pieter-Jan Kindermans & Klaus-Robert Müller

  3. Ghent University, Ghent, Belgium

    Thibault Verhoeven

  4. Korea University, Seongbuk-gu, Seoul, Korea

    Klaus-Robert Müller

  5. Max Planck Institute for Informatics, Saarbrücken, Germany

    Klaus-Robert Müller

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  1. David Hübner
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  2. Pieter-Jan Kindermans
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  3. Thibault Verhoeven
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  4. Klaus-Robert Müller
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  5. Michael Tangermann
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Correspondence to Michael Tangermann .

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Editors and Affiliations

  1. g.tec Medical Engineering GmbH, Schiedlberg, Austria

    Christoph Guger

  2. Department of Health Science and Technology, Aalborg University, Aalborg, Denmark

    Natalie Mrachacz-Kersting

  3. Department of Cognitive Science, University of California, San Diego, CA, USA

    Brendan Z. Allison

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Hübner, D., Kindermans, PJ., Verhoeven, T., Müller, KR., Tangermann, M. (2019). Rethinking BCI Paradigm and Machine Learning Algorithm as a Symbiosis: Zero Calibration, Guaranteed Convergence and High Decoding Performance. In: Guger, C., Mrachacz-Kersting, N., Allison, B. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-05668-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-05668-1_6

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