Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010, Graz, Austria.
Med Biol Eng Comput. 2010 Mar;48(3):229-33. doi: 10.1007/s11517-009-0572-7. Epub 2010 Jan 6.
Bringing a Brain-Computer Interface (BCI) out of the lab one of the main problems has to be solved: to shorten the training time. Finding a solution for this problem, the use of a BCI will be open not only for people who have no choice, e.g., persons in a locked-in state, or suffering from a degenerating nerve disease. By reducing the training time to a minimum, also healthy persons will make use of the system, e.g., for using this kind of control for games. For realizing such a control, the post-movement beta rebound occurring after brisk feet movement was used to set up a classifier. This classifier was then used in a cue-based motor imagery system. After classifier adaptation, a self-paced brain-switch based on brisk foot motor imagery (MI) was evaluated. Four out of six subjects showed that a post-movement beta rebound after feet MI and succeeded with a true positive rate between 69 and 89%, while the positive predictive value was between 75 and 93%.
将脑机接口 (BCI) 从实验室中带出来，其中一个主要问题必须得到解决：缩短训练时间。为了解决这个问题，BCI 将不仅对那些别无选择的人开放，例如闭锁综合征患者或患有神经退行性疾病的人。通过将训练时间减少到最低限度，健康人也将使用该系统，例如，将这种控制用于游戏。为了实现这种控制，使用了快速脚部运动后的运动后β反弹来建立分类器。然后，该分类器被用于基于提示的运动想象系统中。在分类器适应后，评估了基于快速脚部运动想象 (MI) 的自我调节脑开关。6 名受试者中有 4 名显示出脚部 MI 后的运动后β反弹，并以 69%至 89%的真阳性率成功，而阳性预测值在 75%至 93%之间。
