Institute for Knowledge Discovery, BCI Lab, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria.
Med Biol Eng Comput. 2012 Mar;50(3):223-30. doi: 10.1007/s11517-011-0858-4. Epub 2012 Jan 1.
Patients who benefit from Brain-Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accuracy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solutions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an artificial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future movements. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.
受益于脑机接口（BCI）的患者可能难以生成可用于控制应用程序的多个独特脑模式。其他 BCI 问题包括性能、准确性低，并且取决于 BCI 的类型，还需要较长的准备和/或训练时间。本研究旨在展示可能的解决方案。首先，我们使用了仅有一种模式的时间编码运动想象（MI）。其次，我们通过仅记录 20 次主动 MI 试验来减少训练时间，以设置 BCI 分类器。第三，我们研究了一种在连续反馈期间记录错误电位（ErrPs）的方法。十个受试者通过在 1 到 4 秒之间的目标时间段内执行 MI 来控制人工手臂。手臂的后续运动作为连续反馈。为了指示未来的运动，在连续移动的手臂上方安装闪烁的 LED 来添加离散事件，以引出 ErrPs。使用这些事件之后的时间间隔来离线评估 ErrPs。手臂运动的平均错误率为 26.9%。获得的 ErrPs 与以前研究中涉及错误检测的结果相似，检测率高于机会水平，这是一个积极的结果，并鼓励进一步研究。
