@article {2541,
	title = {Review of the BCI Competition IV.},
	journal = {Frontiers in Neuroprosthetics},
	volume = {6},
	year = {2012},
	month = {07/2012},
	pages = {1-31},
	abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.},
	keywords = {BCI, brain-computer interface, competition},
	doi = {10.3389/fnins.2012.00055},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22811657},
	author = {Tangermann, M. and Muller, K.R. and Aertsen, A. and Niels Birbaumer and Christoph Braun and Brunner, Clemens and Leeb, R. and Mehring, C. and Miller, K.J. and Mueller-Putz, G. and Nolte, G. and Pfurtscheller, G. and Preissl, H. and Gerwin Schalk and Schl{\"o}gl, A. and Vidaurre, C. and Waldert, S. and Benjamin Blankertz}
}
@conference {2261,
	title = {Brain-Computer Interaction.},
	booktitle = {5th Intl. Conference on Augmented Cognition},
	year = {2009},
	month = {2009},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p><span style="color: $\#$333333; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Arial, Helvetica, sans-serif; font-size: 13px; line-height: 20px;">Detection and automated interpretation of attention-related or intention-related brain activity carries significant promise for many military and civilian applications. This interpretation of brain activity could provide information about a person{\textquoteright}s intended movements, imagined movements, or attentional focus, and thus could be valuable for optimizing or replacing traditional motor-based communication between a person and a computer or other output devices. We describe here the objective and preliminary results of our studies in this area.</span></p>},
	keywords = {BCI, brain-computer interface, neural engineering, neural prosthesis},
	isbn = {978-3-642-02811-3},
	doi = {10.1007/978-3-642-02812-0_81},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02812-0_81},
	author = {Peter Brunner and Gerwin Schalk}
}
@article {3222,
	title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.},
	journal = {IEEE transactions on bio-medical engineering},
	volume = {51},
	year = {2004},
	month = {06/2004},
	pages = {1044{\textendash}1051},
	abstract = {Interest in developing a new method of man-to-machine communication{\textendash}a brain-computer interface (BCI){\textendash}has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.},
	keywords = {augmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials},
	issn = {0018-9294},
	doi = {10.1109/TBME.2004.826692},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15188876},
	author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schr{\"o}der, Michael and Niels Birbaumer}
}
