%0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T The BCI competition III: Validating alternative approaches to actual BCI problems. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Krusienski, Dean J %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Pfurtscheller, Gert %A Millán, José del R %A Schröder, Michael %A Niels Birbaumer %K Algorithms %K Brain %K Communication Aids for Disabled %K Databases, Factual %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K Software Validation %K Technology Assessment, Biomedical %K User-Computer Interface %X

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 153-9 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792282 %N 2 %R 10.1109/TNSRE.2006.875642 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. %A Jeremy Jeremy Hill %A Lal, T.N %A Schröder, Michael %A Hinterberger, T. %A Wilhelm, Barbara %A Nijboer, F %A Mochty, Ursula %A Widman, Guido %A Elger, Christian %A Schölkopf, B %A Kübler, A. %A Niels Birbaumer %K Algorithms %K Artificial Intelligence %K Cluster Analysis %K Computer User Training %K Electroencephalography %K Evoked Potentials %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Paralysis %K Pattern Recognition, Automated %K User-Computer Interface %X

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 183-6 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792289 %N 2 %R 10.1109/TNSRE.2006.875548 %0 Journal Article %J IEEE Trans Biomed Eng %D 2004 %T The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Curio, Gabriel %A Theresa M Vaughan %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Neuper, Christa %A Pfurtscheller, Gert %A Hinterberger, T. %A Schröder, Michael %A Niels Birbaumer %K Adult %K Algorithms %K Amyotrophic Lateral Sclerosis %K Artificial Intelligence %K Brain %K Cognition %K Databases, Factual %K Electroencephalography %K Evoked Potentials %K Humans %K Reproducibility of Results %K Sensitivity and Specificity %K User-Computer Interface %X Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--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. %B IEEE Trans Biomed Eng %V 51 %P 1044-51 %8 06/2004 %G eng %N 6 %R 10.1109/TBME.2004.826692