Toward enhanced P300 speller performance.

By Dean J. Krusienski, Eric W. Sellers, Dennis J. McFarland, Theresa M. Vaughan, Jonathan R. Wolpaw. Journal of Neuroscience Methods, 2008

773 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b0f8d38305de7e789696a637e92c34e6d7820017

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Single-trial analysis and classification of ERP components - A tutorial (2011, neutral): “, [31, 32, 22]).”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, methodology): “In [102] information from posterior locations such as PO7, PO8, and Oz were added to an SWLDA classifier.”
A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation (2009, methodology): “For each of these subproblems, the stepwise linear discriminant analysis (SWLDA) was used, extensively studied for P300 classification problems [23], and used with very good results in online communication using visual stimulation [28] and auditory stimulation [29].”
A brain-computer interface for long-term independent home use (2010, methodology): “Similar stability has been found in our laboratory studies of able-bodied subjects, where the same set performed well in two sessions 11 months apart (18).”
Bridging the brain to the world: a perspective on neural interface systems. (2008, positive): “Classification with a slow rhythm or P300 system is very time-consuming, taking seconds to tens of seconds per choice, and is more error prone, although new decoding schemes have improved accuracy ( Krusienski et al., 2008 ).”
A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. (2008, methodology): “The other features used in Phase II were suggested by Krusienski et al. (2008).”
A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns (2010, methodology): “Only electrodes Fz, Cz, P3, Pz, P4, PO7, PO8, and Oz (Sharbrough et al., 1991) were used for BCI operation (Krusienski et al., 2008).”
(C)overt attention and visual speller design in an ERP-based brain-computer interface (2010, result): “This confirms earlier conjectures [38,49] and it is also in accordance with [50] who showed that classifying on posterior electrodes in addition to the classical P3 sites improves classification performance.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “The signal features extracted can be in the time-domain or the frequency-domain [6,11,49,77-80], or both [81].”
A comparison of classification techniques for the P300 Speller (2006, methodology): “For this study, the input features were selected to presumably contain the essence of the P300 Speller response for discrimination [9].”
Brain-Computer Interfaces for Communication and Control (2011, neutral): “Several groups have since further developed this BCI method.(13) Wolpaw et al.”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, background): “However, Krusienski et al. showed that occipital sites are more important [ 23 ].”