The BCI competition III: validating alternative approaches to actual BCI problems

By Benjamin Blankertz, K. Müller, Dean J. Krusienski, Gerwin Schalk, Jonathan R. Wolpaw, A. Schlögl, Gert Pfurtscheller, José del R. Millán, M. Schröder, Niels Birbaumer. IEEE transactions on neural systems and rehabilitation engineering, 2006

935 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/7ba77cf82b14fdd9e694fb10f45bcd2e8ad3921e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction (2008, background): “A detailed description of the recording procedure can be found in [3].”
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] .”
Toward enhanced P300 speller performance. (2008, positive): “However, the posterior response seems to provide significant additional discriminative information for the P300 speller [1][2][3][4][11][21][24].”
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010, methodology): “Experiments are carried out on data set IVa of BCI Competition III [34], [37].”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, methodology): “2) Data set IIIa, BCI competition III: Data set IIIa [23], from BCI competition III [6], comprises EEG signals from 3 subjects who performed left hand, right hand, foot and tongue MI. EEG signals were recorded using 60 electrodes.”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, methodology): “Tables III and IV summarize the performance (classiﬁcation accuracy and number of selected channels) of all subjects.”
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition (2013, methodology): “We report here the outcome of this challenge, as well as baseline performance using different classification techniques.”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, methodology): “Data set II from the third BCI competition was used for testing the different models [26].”
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces (2017, methodology): “For each file, the data loaded in MATLAB generate a 4-D matrix named “data” with dimensions of [64, 1500, 40, 6].”