Prediction of brain-computer interface aptitude from individual brain structure
Objective: Brain-computer interfaces (BCIs) 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 apt...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-04-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00105/full |
| _version_ | 1819266615327326208 |
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| author | Sebastian eHalder Balint eVarkuti Martin eBogdan Andrea eKübler Wolfgang eRosenstiel Ranganatha eSitaram Niels eBirbaumer |
| author_facet | Sebastian eHalder Balint eVarkuti Martin eBogdan Andrea eKübler Wolfgang eRosenstiel Ranganatha eSitaram Niels eBirbaumer |
| author_sort | Sebastian eHalder |
| collection | DOAJ |
| description | Objective: Brain-computer interfaces (BCIs) 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 diffusion tensor imaging (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. |
| first_indexed | 2024-12-23T21:04:05Z |
| format | Article |
| id | doaj.art-96e9573ce1da4130afa1f06807455264 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-23T21:04:05Z |
| publishDate | 2013-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-96e9573ce1da4130afa1f068074552642022-12-21T17:31:17ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612013-04-01710.3389/fnhum.2013.0010543764Prediction of brain-computer interface aptitude from individual brain structureSebastian eHalder0Balint eVarkuti1Martin eBogdan2Andrea eKübler3Wolfgang eRosenstiel4Ranganatha eSitaram5Niels eBirbaumer6University of WürzburgUniversity of TübingenUniversity of LeipzigUniversity of WürzburgUniversity of TübingenUniversity of FloridaUniversity of TübingenObjective: Brain-computer interfaces (BCIs) 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 diffusion tensor imaging (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.http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00105/fullAptitudeDTIBCIMotor Imageryfractional anisotropy |
| spellingShingle | Sebastian eHalder Balint eVarkuti Martin eBogdan Andrea eKübler Wolfgang eRosenstiel Ranganatha eSitaram Niels eBirbaumer Prediction of brain-computer interface aptitude from individual brain structure Frontiers in Human Neuroscience Aptitude DTI BCI Motor Imagery fractional anisotropy |
| title | Prediction of brain-computer interface aptitude from individual brain structure |
| title_full | Prediction of brain-computer interface aptitude from individual brain structure |
| title_fullStr | Prediction of brain-computer interface aptitude from individual brain structure |
| title_full_unstemmed | Prediction of brain-computer interface aptitude from individual brain structure |
| title_short | Prediction of brain-computer interface aptitude from individual brain structure |
| title_sort | prediction of brain computer interface aptitude from individual brain structure |
| topic | Aptitude DTI BCI Motor Imagery fractional anisotropy |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2013.00105/full |
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