Title
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Abstract
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.
Year
DOI
Venue
2011
10.1155/2011/519868
Comp. Int. and Neurosc.
Keywords
Field
DocType
intelligence,and,computational,neurofeedback,electroencephalography,neuroscience
Movement disorders,Computer science,Brain–computer interface,Physical therapy,Artificial intelligence,Physical medicine and rehabilitation,Classifier (linguistics),Electroencephalography,Pattern recognition,Stroke,Typing,Linear classifier,Neurofeedback
Journal
Volume
ISSN
Citations 
2011,
1687-5265
27
PageRank 
References 
Authors
2.07
10
4
Name
Order
Citations
PageRank
Nikolay V. Manyakov111111.82
Nikolay Chumerin2748.42
Adrien Combaz3677.30
Marc M. Van Hulle462269.75