@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{ZieheMuellerNolteetal.2000,
  author    = {Ziehe, Andreas and M{\"u}ller, Klaus-Robert and Nolte, G. and Mackert, B.-M. and Curio, Gabriel},
  title     = {Artifact reduction in magnetoneurography based on time-delayed second-order correlations},
  year      = {2000},
  language  = {en}
}
@article{WuebbelerZieheMackertetal.2000,
  author    = {W{\"u}bbeler, G. and Ziehe, Andreas and Mackert, B.-M. and M{\"u}ller, Klaus-Robert and Trahms, L. and Curio, Gabriel},
  title     = {Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans},
  year      = {2000},
  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{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{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{LemmCurioHlushchuketal.2006,
  author    = {Lemm, Steven and Curio, Gabriel and Hlushchuk, Yevhen and M{\"u}ller, Klaus-Robert},
  title     = {Enhancing the signal-to-noise ratio of ICA-based extracted ERPs},
  issn      = {0018-9294},
  doi       = {10.1109/Tbme.2006.870258},
  year      = {2006},
  abstract  = {When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings},
  language  = {en}
}
@article{BlankertzDornhegeKrauledatetal.2006,
  author    = {Blankertz, Benjamin and Dornhege, Guido and Krauledat, Matthias and M{\"u}ller, Klaus-Robert and Kunzmann, Volker and Losch, Florian and Curio, Gabriel},
  title     = {The Berlin brain-computer interface : EEG-based communication without subject training},
  issn      = {1534-4320},
  doi       = {10.1109/Tnsre.2006.875557},
  year      = {2006},
  abstract  = {The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left-versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems},
  language  = {en}
}