@article{LemmBlankertzCurioetal.2005, author = {Lemm, Steven and Blankertz, Benjamin and Curio, Gabriel and M{\"u}ller, Klaus-Robert}, title = {Spatio-spectral filters for improving the classification of single trial EEG}, issn = {0018-9294}, year = {2005}, abstract = {Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements}, language = {en} } @article{DornhegeBlankertzCurioetal.2004, author = {Dornhege, Guido and Blankertz, Benjamin and Curio, Gabriel and M{\"u}ller, Klaus-Robert}, title = {Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms}, year = {2004}, abstract = {Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances}, language = {en} } @article{BlankertzMuellerCurioetal.2004, author = {Blankertz, Benjamin and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Vaughan, Theresa M. and Schalk, Gerwin and Wolpaw, Jonathan R. and Schlogl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schroder, Michael and Birbaumer, Niels}, title = {The BCI competition 2003 : Progress and perspectives in detection and discrimination of EEG single trials}, issn = {0018-9294}, year = {2004}, abstract = {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}, language = {en} } @article{HarmelingMeineckeMueller2004, author = {Harmeling, Stefan and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert}, title = {Injecting noise for analysing the stability of ICA components}, issn = {0165-1684}, year = {2004}, abstract = {Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data. (C) 2003 Elsevier B.V. All rights reserved}, language = {en} } @article{MuellerVigarioMeineckeetal.2004, author = {M{\"u}ller, Klaus-Robert and Vigario, R. and Meinecke, Frank C. and Ziehe, Andreas}, title = {Blind source separation techniques for decomposing event-related brain signals}, issn = {0218-1274}, year = {2004}, abstract = {Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data}, language = {en} } @article{SugiyamaKawanabeMueller2004, author = {Sugiyama, Masashi and Kawanabe, Motoaki and M{\"u}ller, Klaus-Robert}, title = {Trading variance reduction with unbiasedness : the regularized subspace information criterion for robust model selection in kernel regression}, issn = {0899-7667}, year = {2004}, abstract = {A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results}, language = {en} } @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{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} } @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{ZienRaetschMikaetal.2000, author = {Zien, Alexander and R{\"a}tsch, Gunnar and Mika, Sebastian and Sch{\"o}lkopf, Bernhard and Lengauer, Thomas and M{\"u}ller, Klaus-Robert}, title = {Engineering support vector machine kernels that recognize translation initiation sites}, issn = {1367-4803}, year = {2000}, language = {en} }