@article{HoehneHolzStaigerSaelzeretal.2014,
  author    = {H{\"o}hne, Johannes and Holz, Elisa and Staiger-S{\"a}lzer, Pit and M{\"u}ller, Klaus-Robert and K{\"u}bler, Andrea and Tangermann, Michael},
  title     = {Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution},
  series = {PLoS ONE},
  volume    = {9},
  journal   = {PLoS ONE},
  number    = {8},
  doi       = {10.1371/journal.pone.0104854},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-119331},
  pages     = {e104854},
  year      = {2014},
  abstract  = {Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.},
  language  = {en}
}
@article{ZellerMuellerGutberletetal.2013,
  author    = {Zeller, Mario and M{\"u}ller, Alexander and Gutberlet, Marcel and Nichols, Thomas and Hahn, Dietbert and K{\"o}stler, Herbert and Bartsch, Andreas J.},
  title     = {Boosting BOLD fMRI by K-Space Density Weighted Echo Planar Imaging},
  series = {PLoS ONE},
  journal   = {PLoS ONE},
  doi       = {10.1371/journal.pone.0074501},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-97233},
  year      = {2013},
  abstract  = {Functional magnetic resonance imaging (fMRI) has become a powerful and influential method to non-invasively study neuronal brain activity. For this purpose, the blood oxygenation level-dependent (BOLD) effect is most widely used. T2* weighted echo planar imaging (EPI) is BOLD sensitive and the prevailing fMRI acquisition technique. Here, we present an alternative to its standard Cartesian recordings, i.e. k-space density weighted EPI, which is expected to increase the signal-to-noise ratio in fMRI data. Based on in vitro and in vivo pilot measurements, we show that fMRI by k-space density weighted EPI is feasible and that this new acquisition technique in fact boosted spatial and temporal SNR as well as the detection of local fMRI activations. Spatial resolution, spatial response function and echo time were identical for density weighted and conventional Cartesian EPI. The signal-to-noise ratio gain of density weighting can improve activation detection and has the potential to further increase the sensitivity of fMRI investigations.},
  language  = {en}
}