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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

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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