Year: 2013
Type: article
Study design: Observational Study
Abstract: 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.
Source: Frontiers in Human Neuroscience
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,231
Awards: G1345660544, G1681590414, Brain-neural computer interfaces on track to home – Development of a practical generation of BNCI for independent home use, G6362701848, G6431786112, Verbundprojekt: Bernstein Fokus Neurotechnologie - Hybrid Brain - Teilprojekte A2-A3, C1-C3, Bidirectional cortical communication interface
OpenAlex ID: W2172012103