@article{HalderVarkutiBogdanetal.2013,
  author    = {Halder, Sebastian and Varkuti, Balint and Bogdan, Martin and K{\"u}bler, Andrea and Rosenstiel, Wolfgang and Sitaram, Ranganatha and Birbaumer, Niels},
  title     = {Prediction of brain-computer interface aptitude from individual brain structure},
  series = {Frontiers in Human Neuroscience},
  journal   = {Frontiers in Human Neuroscience},
  doi       = {10.3389/fnhum.2013.00105},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96558},
  year      = {2013},
  abstract  = {Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use.},
  language  = {en}
}