| Summary: | Objective: Brain-computer interfaces (BCIs) 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 diffusion tensor imaging (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.
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