@article{HarmelingDornhegeTaxetal.2006,
  author    = {Harmeling, Stefan and Dornhege, Guido and Tax, David and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert},
  title     = {From outliers to prototypes : Ordering data},
  issn      = {0925-2312},
  doi       = {10.1016/j.neucom.2005.05.015},
  year      = {2006},
  abstract  = {We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.},
  language  = {en}
}
@article{BlankertzMuellerKrusienskietal.2006,
  author    = {Blankertz, Benjamin and M{\"u}ller, Klaus-Robert and Krusienski, Dean and Schalk, Gerwin and Wolpaw, Jonathan R. and Schl{\"o}gl, Alois and Pfurtscheller, Gert and Millan, Jos{\´e} del R. and Schr{\"o}der, Michael and Birbaumer, Niels},
  title     = {The BCI competition III : validating alternative approaches to actual BCI problems},
  issn      = {1534-4320},
  doi       = {10.1109/Tnsre.2006.875642},
  year      = {2006},
  abstract  = {A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. 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.},
  language  = {en}
}
@article{DornhegeBlankertzKrauledatetal.2006,
  author    = {Dornhege, Guido and Blankertz, Benjamin and Krauledat, Matthias and Losch, Florian and Curio, Gabriel and M{\"u}ller, Klaus-Robert},
  title     = {Combined optimization of spatial and temporal filters for improving brain-computer interfacing},
  series = {IEEE transactions on bio-medical electronics},
  volume    = {53},
  journal   = {IEEE transactions on bio-medical electronics},
  number    = {11},
  publisher = {IEEE},
  address   = {New York},
  issn      = {0018-9294},
  doi       = {10.1109/TBME.2006.883649},
  pages     = {2274 -- 2281},
  year      = {2006},
  abstract  = {Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11\%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.},
  language  = {en}
}
@article{MontavonBraunKruegeretal.2013,
  author    = {Montavon, Gregoire and Braun, Mikio L. and Kr{\"u}ger, Tammo and M{\"u}ller, Klaus-Robert},
  title     = {Analyzing local structure in Kernel-Based learning},
  series = {IEEE signal processing magazine},
  volume    = {30},
  journal   = {IEEE signal processing magazine},
  number    = {4},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Piscataway},
  issn      = {1053-5888},
  doi       = {10.1109/MSP.2013.2249294},
  pages     = {62 -- 74},
  year      = {2013},
  language  = {en}
}