An efficient P300-based brain–computer interface for disabled subjects

[1]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[2]  Neil D. Lawrence,et al.  Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis , 2006, J. Mach. Learn. Res..

[3]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[4]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[5]  T. N. Lal,et al.  Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[8]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.

[9]  Matthias Kaper.  P300 based brain computer interfacing , 2006 .

[10]  Touradj Ebrahimi,et al.  Spatial filters for the classification of event-related potentials , 2006, ESANN.

[11]  Alain Rakotomamonjy,et al.  Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances , 2005, ICANN.

[12]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[13]  U. Hoffmann,et al.  A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[14]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  T. Ebrahimi,et al.  A Boosting Approach to P 300 Detection with Application to Brain-Computer Interfaces , 2005 .

[16]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[17]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[19]  U. Hoffmann,et al.  Application of the evidence framework to brain-computer interfaces , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  J.D. Bayliss,et al.  Use of the evoked potential P3 component for control in a virtual apartment , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[22]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.

[23]  H. Timothy Bunnell,et al.  Toward a P300-based Computer Interface , 2002 .

[24]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[25]  M. Carrillo-de-la-Peña,et al.  The effect of motivational instructions on P300 amplitude , 2000, Neurophysiologie Clinique/Clinical Neurophysiology.

[26]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[27]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[28]  M. Posner,et al.  The attention system of the human brain. , 1990, Annual review of neuroscience.

[29]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[30]  Q. Tian,et al.  Comparison of statistical pattern-recognition algorithms for hybrid processing. II: Eigenvector-based algorithm , 1988 .

[31]  E. Donchin,et al.  On quantifying surprise: the variation of event-related potentials with subjective probability. , 1977, Psychophysiology.

[32]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.