@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}
}
@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{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}
}