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A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI

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  • Pieter-Jan Kindermans
  • David Verstraeten
  • Benjamin Schrauwen

Abstract

This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datasets for validation of our method and show that our unsupervised classifier performs competitively with supervised state-of-the-art spellers. Finally, we demonstrate the added value of our method in different experimental settings which reflect realistic usage situations of increasing difficulty and which would be difficult or impossible to tackle with existing supervised or adaptive methods.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0033758
    DOI: 10.1371/journal.pone.0033758
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    Cited by:

    1. Iñaki Iturrate & Jonathan Grizou & Jason Omedes & Pierre-Yves Oudeyer & Manuel Lopes & Luis Montesano, 2015. "Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    2. 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.

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