Simultaneous Detection of P300 and Steady-State Visually Evoked Potentials for Hybrid Brain-Computer Interface
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[1] Arne Robben,et al. Subject-adaptive steady-state visual evoked potential detection for brain-computer interface , 2011, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems.
[2] L. R. Quitadamo,et al. Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study , 2010, Brain Topography.
[3] E. John,et al. Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.
[4] A. Kübler,et al. ERPs contributing to classification in the ”P300” BCI , 2011 .
[5] Ying Sun,et al. Asynchronous P300 BCI: SSVEP-based control state detection , 2010, 2010 18th European Signal Processing Conference.
[6] T. Jaeger,et al. Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. , 2008, Journal of memory and language.
[7] N. Birbaumer,et al. The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..
[8] Steven Laureys,et al. A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome , 2013, PloS one.
[9] E Donchin,et al. Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[10] 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.
[11] E. W. Sellers,et al. Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.
[12] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[13] J. Wolpaw,et al. A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.
[14] W. A. Sarnacki,et al. Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.
[15] Dong Ming,et al. A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature , 2013, Journal of neural engineering.
[16] Giuseppe Andreoni,et al. A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication , 2009, Comput. Intell. Neurosci..
[17] Fanglin Chen,et al. A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm , 2013, Journal of neural engineering.
[18] T. Hothorn,et al. Simultaneous Inference in General Parametric Models , 2008, Biometrical journal. Biometrische Zeitschrift.
[19] N. Birbaumer,et al. Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.
[20] Ivan Volosyak,et al. Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.
[21] Fanglin Chen,et al. A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP , 2014, IEEE Transactions on Biomedical Engineering.
[22] E Donchin,et al. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[23] A. Cichocki,et al. Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives , 2010, Progress in Neurobiology.
[24] D. Bates,et al. Mixed-Effects Models in S and S-PLUS , 2001 .
[25] G.F. Inbar,et al. An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[26] D. Regan. Some characteristics of average steady-state and transient responses evoked by modulated light. , 1966, Electroencephalography and clinical neurophysiology.
[27] S. Sathiya Keerthi,et al. A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..
[28] Jonathan R Wolpaw,et al. A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.
[29] Marina Schmid,et al. An Introduction To The Event Related Potential Technique , 2016 .
[30] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[31] R. Baayen,et al. Mixed-effects modeling with crossed random effects for subjects and items , 2008 .
[32] N. Birbaumer,et al. Predictability of Brain-Computer Communication , 2004 .
[33] C. Herrmann. Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.
[34] C Neuper,et al. A comparison of three brain–computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals , 2011, Journal of neural engineering.
[35] D G Pelli,et al. The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.
[36] Katrien Vanderperren,et al. Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes , 2011 .
[37] Xiaorong Gao,et al. An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.
[38] Brendan Z. Allison,et al. The Hybrid BCI , 2010, Frontiers in Neuroscience.