title: Prediction of brain-computer interface aptitude from individual brain structure.
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.
  
  
  CONCLUSIONS
  This confirms that structural brain traits contribute to individual performance in BCI use.
authors.affiliation: Department of Psychology I, University of Würzburg Würzburg, Germany ; Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen Tübingen, Germany ; Wilhelm-Schickard Institute for Computer Science, University of Tübingen Tübingen, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Halder
authors.forename: B
authors.initials: B
authors.lastname: Varkuti
authors.forename: M
authors.initials: M
authors.lastname: Bogdan
authors.forename: A
authors.initials: A
authors.lastname: Kübler
authors.forename: W
authors.initials: W
authors.lastname: Rosenstiel
authors.forename: R
authors.initials: R
authors.lastname: Sitaram
authors.forename: N
authors.initials: N
authors.lastname: Birbaumer
keywords: BCI; DTI; aptitude; fractional anisotropy; motor imagery
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