• search hit 6 of 7
Back to Result List

Prediction of brain-computer interface aptitude from individual brain structure

Please always quote using this URN: urn:nbn:de:bvb:20-opus-96558
  • 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 ofObjective: 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.show moreshow less

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author: Sebastian Halder, Balint Varkuti, Martin Bogdan, Andrea Kübler, Wolfgang Rosenstiel, Ranganatha Sitaram, Niels Birbaumer
URN:urn:nbn:de:bvb:20-opus-96558
Document Type:Journal article
Faculties:Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie
Language:English
Parent Title (English):Frontiers in Human Neuroscience
Year of Completion:2013
Source:Frontiers in Human Neuroscience (2013) 7: 105, doi:10.3389/fnhum.2013.00105
DOI:https://doi.org/10.3389/fnhum.2013.00105
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Tag:BCI; DTI; aptitude; fractional anisotropy; motor imagery
Release Date:2014/04/29
EU-Project number / Contract (GA) number:288566
EU-Project number / Contract (GA) number:227632
OpenAIRE:OpenAIRE
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2013
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung