TY  - JOUR
A1  - Halder, Sebastian
A1  - Varkuti, Balint
A1  - Bogdan, Martin
A1  - Kübler, Andrea
A1  - Rosenstiel, Wolfgang
A1  - Sitaram, Ranganatha
A1  - Birbaumer, Niels
T1  - Prediction of brain-computer interface aptitude from individual brain structure
JF  - Frontiers in Human Neuroscience
N2  - 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.
KW  - BCI
KW  - motor imagery
KW  - aptitude
KW  - DTI
KW  - fractional anisotropy
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-96558
ER  -