AUTHOR=Halder Sebastian , Varkuti Balint , Bogdan Martin , Kübler Andrea , Rosenstiel Wolfgang , Sitaram Ranganatha , Birbaumer Niels 

TITLE=Prediction of brain-computer interface aptitude from individual brain structure

JOURNAL=Frontiers in Human Neuroscience

VOLUME=7

YEAR=2013

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2013.00105

DOI=10.3389/fnhum.2013.00105

ISSN=1662-5161

ABSTRACT=<p><bold>Objective:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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).</p><p><bold>Conclusions:</bold> 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.</p><p><bold>Significance:</bold> This confirms that structural brain traits contribute to individual performance in BCI use.</p>