Title: Comparison of classification methods for P300 Brain-Computer Interface on disabled subjects
Authors: Manyakov, Nikolay V. ×
Chumerin, Nikolay
Combaz, Adrien
Van Hulle, Marc #
Issue Date: Sep-2011
Publisher: Hindawi Publishing Corp.
Series Title: Computational Intelligence and Neuroscience vol:2011 pages:1-12
Article number: 519868
Abstract: In this paper, we report on tests with the P300 Brain-Computer Interface (BCI) typing paradigm on neurological patients suffering from motor and speech disabilities. We investigate the accuracy of different classifiers: Fisher's Linear Discriminant Analysis (LDA), Bayesian Linear Discriminant Analysis (BLDA), Stepwise Linear Discriminant Analysis (SLDA), a method based on Feature Extraction (FE), linear Support Vector Machine (SVM), Gaussian kernel Support Vector Machine (nSVM) and multi-layer perceptron (NN). Tests were performed on patients suffering from a Amyotrophic Lateral Sclerosis (ALS), a middle cerebral artery (MCA) stroke, and a Subarachnoid Hemorrhage (SAH), both in on-line and in off-line mode. Our results show that BLDA, in general, yields a higher classification accuracy than the other classifiers, except for nSVM in some cases, at least for our group of patients.
ISSN: 1687-5265
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
Laboratory for Neuro- and Psychofysiology
× corresponding author
# (joint) last author

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