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True Zero-Training Brain-Computer Interfacing – An Online Study

Author

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  • Pieter-Jan Kindermans
  • Martijn Schreuder
  • Benjamin Schrauwen
  • Klaus-Robert Müller
  • Michael Tangermann

Abstract

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.

Suggested Citation

  • Pieter-Jan Kindermans & Martijn Schreuder & Benjamin Schrauwen & Klaus-Robert Müller & Michael Tangermann, 2014. "True Zero-Training Brain-Computer Interfacing – An Online Study," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
  • Handle: RePEc:plo:pone00:0102504
    DOI: 10.1371/journal.pone.0102504
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    References listed on IDEAS

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    1. Pieter-Jan Kindermans & David Verstraeten & Benjamin Schrauwen, 2012. "A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-12, April.
    2. N. Birbaumer & N. Ghanayim & T. Hinterberger & I. Iversen & B. Kotchoubey & A. Kübler & J. Perelmouter & E. Taub & H. Flor, 1999. "A spelling device for the paralysed," Nature, Nature, vol. 398(6725), pages 297-298, March.
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    Cited by:

    1. Bangyan Zhou & Xiaopei Wu & Zhao Lv & Lei Zhang & Xiaojin Guo, 2016. "A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-20, September.

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