The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

By Benjamin Blankertz, K. Müller, Gabriel Curio, Theresa M. Vaughan, Gerwin Schalk, Jonathan R. Wolpaw, Alois Schlögl, Christa Neuper, Gert Pfurtscheller, Thilo Hinterberger and 2 more. IEEE Transactions on Biomedical Engineering, 2004

727 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6e91049fd30ee024a5d7f97892c2be4fae3747dc

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Citations in the corpus

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, methodology): “The -CWT method was also tested on two data sets provided by the BCI group at the Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany [1].”
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms (2004, background): “It was an interesting outcome of the BCI Competition 2003 ([12]) that on all 5 different kinds of BCI data sets linear me thods either achieved the minimum test error among the competing algorithms or were at least not significantly worse th an the best non-linear method, cf.”
The BCI competition III: validating alternative approaches to actual BCI problems (2006, background): “, [17] and [9]) where in most cases the top competitiors were very”
Toward enhanced P300 speller performance. (2008, unknown): “However, the posterior response seems to provide significant additional discriminative information for the P300 speller [1][2][3][4][11][21][24].”
Introduction to machine learning for brain imaging (2011, background): “This has allowed a number of interested scientists from the fields of signal processing and machine learning to develop new methods and to study experimental data even without having access to expensive measurement technology (Sajda et al., 2003; Blankertz et al., 2004; Blankertz et al., 2006b).”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “The authors used two public data-sets (BCI-C II-3 [102] and BCI-C IV-2b [101]) containing two MI classes with one and nine subjects, achieving 90.0% and 77.6% accuracy, respectively.”
xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface (2009, methodology): “The experiment paradigm was controlled by the BCI2000 software and was similar to the one used for the BCI Competition 2003-P300 Speller dataset [ 25 ].”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, result): “These two subjects have an average P300 response compared to other studies like the BCI competition 2003 [44].”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, methodology): “CSP has been proven to be one of the most popular and efficient algorithms for BCI design, notably during BCI competitions [5], [6].”
A practical VEP-based brain-computer interface. (2006, unknown): “Event-related potentials, mu and beta rhythms, event-related synchronization and desynchronization, slow cortical potentials, and visual evoked potentials (VEP) are commonly used signals in EEG-based BCIs [1]–[3].”
A review of classification algorithms for EEG-based brain–computer interfaces (2007, methodology): “The rst one corresponds to studies for which it is possible to objectively compare the algorithms since the EEG data used are benchmark data, such as data from the BCI competition 2003 [ 82 ] or personal data on which several classiers are compared.”
Brain–computer interface systems: progress and prospects (2007, background): “Data analysis competitions have already provided an international venue for comparing signal processing methods and will probably continue to do so [155,156] .”
BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm (2004, methodology): “The algorithm performed pretty well within the BCI-Competition 2003 [3]: It inferred the correct words and, thus, qualified as a winner.”