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Computational Intelligence and Neuroscience
Volume 2011, Article ID 519868, 12 pages
Research Article

Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

Laboratorium voor Neuro- en Psychofysiologie, K.U.Leuven, Campus Gasthuisberg, O&N 2, Bus 1021, Herestraat 49, B-3000 Leuven, Belgium

Received 14 March 2011; Revised 26 May 2011; Accepted 4 July 2011

Academic Editor: Laura Astolfi

Copyright © 2011 Nikolay V. Manyakov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.