a bayesian model for exploiting application constraints to enable unsupervised training of a p300-based bci利用贝叶斯模型应用约束,使p300-based bci的无监督培训.pdfVIP

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a bayesian model for exploiting application constraints to enable unsupervised training of a p300-based bci利用贝叶斯模型应用约束,使p300-based bci的无监督培训.pdf

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a bayesian model for exploiting application constraints to enable unsupervised training of a p300-based bci利用贝叶斯模型应用约束,使p300-based bci的无监督培训

A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI Pieter-Jan Kindermans*, David Verstraeten, Benjamin Schrauwen Electronics and Information Systems, Ghent University, Ghent, Belgium 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. Citation: Kindermans P-J, Verstraeten D, Schrauwen B (2012) A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300- based BCI. PLoS ONE 7(4): e33758. doi:10.1371/journal.pone.0033758 Editor: Pedro Antonio Valdes-Sosa, Cuban Neuroscience Center, Cuba Received October 19, 2011; Accepted February 16, 2012; Published April 4, 2012 Copyright: 2012 Kindermans et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was partially funded by the BOF-GOA Project Home-MATE funded by the Ghent University Special Research Fund. http://www.ugent.be. No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the