Related Experiment Video
Updated: May 12, 2026
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Prediction of brain-computer interface aptitude from individual brain structure
S Halder1, B Varkuti, M Bogdan
1Department 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.
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.
