@article{ParraSpenceSajdaetal.2000, author = {Parra, L. and Spence, C. and Sajda, P. and Ziehe, Andreas and M{\"u}ller, Klaus-Robert}, title = {Unmixing hyperspectral data}, year = {2000}, language = {en} } @article{KohlmorgenMuellerRittwegeretal.2000, author = {Kohlmorgen, J. and M{\"u}ller, Klaus-Robert and Rittweger, J. and Pawelzik, K.}, title = {Identification of nonstationary dynamics in physiological recordings}, year = {2000}, language = {en} } @article{MikaRaetschWestonetal.2000, author = {Mika, Sebastian and R{\"a}tsch, Gunnar and Weston, J. and Sch{\"o}lkopf, B. and Smola, Alexander J. and M{\"u}ller, Klaus-Robert}, title = {Invariant feature extraction and classification in kernel spaces}, year = {2000}, language = {en} } @article{RaetschSchoelkopfSmolaetal.2000, author = {R{\"a}tsch, Gunnar and Sch{\"o}lkopf, B. and Smola, Alexander J. and Mika, Sebastian and Onoda, T. and M{\"u}ller, Klaus-Robert}, title = {Robust ensemble learning for data analysis}, year = {2000}, language = {en} } @article{OnodaRaetschMueller2000, author = {Onoda, T. and R{\"a}tsch, Gunnar and M{\"u}ller, Klaus-Robert}, title = {An asymptotic analysis and improvement of AdaBoost in the binary classification case (in Japanese)}, year = {2000}, 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} }