Prediction of brain-computer interface aptitude from individual brain structure.
Prediction of brain-computer interface aptitude from individual brain structure.
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DOI：
10.3389/fnhum.2013.00105
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发表时间：
2013
影响因子：
2.9
通讯作者：
Birbaumer N
中科院分区：
文献类型：
--
作者：
Halder S;Varkuti B;Bogdan M;Kübler A;Rosenstiel W;Sitaram R;Birbaumer N
目的：脑机接口(BCI)为运动系统受损患者提供了一种非肌肉沟通渠道。相当数量的BCI用户不能在适当的时间获得对BCI系统的自愿控制。这使得可以用来确定用户资质的方法变得必要。方法：我们假设受累脑白质连接的完整性和连通性可以作为个体BCI表现的预测因子。因此，我们分析了来自解剖扫描和DTI的结构数据，BCI使用者根据他们的整体表现将其分为高BCI-智能倾向组和低BCI-智能倾向组。结果：使用机器学习分类方法，我们识别出区分结构的大脑特征特征，并将最好的特征与个体BCI表现的连续测量相关联。预测每个参与者的能力倾向组是可能的，几乎完全准确(一个误差)。结论：组织容量分析只能得出较差的分类结果。相反，深部白质结构的结构完整性和髓鞘形成质量，如胼胝体、扣带和额枕上束，与个体的BCI表现呈正相关。意义：这证实了大脑结构特征对个人在脑机接口使用中的表现有贡献。
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.
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DOI：
10.1016/j.clinph.2008.06.019
发表时间：
2008-11
期刊：
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
影响因子：
--
作者：
Kübler A;Birbaumer N
通讯作者：
Birbaumer N
影响因子：
10.6
作者：
Caria, Andrea;Sitaram, Ranganatha;Birbaumer, Niels
通讯作者：
Birbaumer, Niels
影响因子：
4.7
作者：
Kleih, S. C.;Nijboer, F.;Kuebler, A.
通讯作者：
Kuebler, A.
影响因子：
9.9
作者：
Kübler, A;Nijboer, F;Wolpaw, JR
通讯作者：
Wolpaw, JR
影响因子：
2.6
作者：
Hammer, Eva Maria;Halder, Sebastian;Kuebler, Andrea
通讯作者：
Kuebler, Andrea