The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
P300 detection based on feature extraction in on-line brain-computer interface
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Online detection of p300 and error potentials in a BCI speller
Computational Intelligence and Neuroscience - Special issue on processing of brain signals by using hemodynamic and neuroelectromagnetic modalities
Decoding stimulus-reward pairing from local field potentials recorded from monkey visual cortex
IEEE Transactions on Neural Networks
Maximization of Mutual Information for Supervised Linear Feature Extraction
IEEE Transactions on Neural Networks
Brain-computer interface research at Katholieke Universiteit Leuven
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Feasibility of error-related potential detection as novelty detection problem in p300 mind spelling
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Dynamic stopping improves the speed and accuracy of a p300 speller
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
The zigzag paradigm: a new P300-based brain computer interface
Proceedings of the 15th ACM on International conference on multimodal interaction
Artificial Intelligence in Medicine
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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.