Prediction of brain-computer interface aptitude from individual brain structure

S. Halder, B. Varkuti, M. Bogdan, A. Kübler, W. Rosenstiel, R. Sitaram, N. Birbaumer

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalFrontiers in Human Neuroscience
Issue numberAPR 2013
Publication statusPublished - Apr 2 2013


  • Aptitude
  • BCI
  • DTI
  • Fractional anisotropy
  • Motor imagery

ASJC Scopus subject areas

  • Biological Psychiatry
  • Behavioral Neuroscience
  • Neuropsychology and Physiological Psychology
  • Psychiatry and Mental health
  • Neurology


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