@article{HalderHammerKleihetal.2013,
  author    = {Halder, Sebastian and Hammer, Eva Maria and Kleih, Sonja Claudia and Bogdan, Martin and Rosenstiel, Wolfgang and Birbaumer, Niels and K{\"u}bler, Andrea},
  title     = {Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude},
  series = {PLoS ONE},
  volume    = {8},
  journal   = {PLoS ONE},
  number    = {2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130327},
  pages     = {e53513},
  year      = {2013},
  abstract  = {Objective Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. Methods Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. Results Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. Conclusions Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. Significance Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.},
  language  = {en}
}
@article{MuenssingerHalderKleihetal.2010,
  author    = {M{\"u}nßinger, Jana I. and Halder, Sebastian and Kleih, Sonja C. and Furdea, Adrian and Raco, Valerio and H{\"o}sle, Adi and K{\"u}bler, Andrea},
  title     = {Brain Painting: first evaluation of a new brain-computer interface application with ALS-patients and healthy volunteers},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68168},
  year      = {2010},
  abstract  = {Brain-computer interfaces (BCIs) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user-friendliness of P300-Brain Painting, a new BCI application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A colored matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black and white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89\% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (colored matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black and white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS-patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.},
  subject      = {Psychologie},
  language  = {en}
}