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Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

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  • Iñaki Iturrate
  • Jonathan Grizou
  • Jason Omedes
  • Pierre-Yves Oudeyer
  • Manuel Lopes
  • Luis Montesano

Abstract

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0131491
    DOI: 10.1371/journal.pone.0131491
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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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