Department Empirical Inference, Max Planck Institute for Intelligent Systems, Spemannstr. 38, 72076 Tübingen, Germany.
J Neural Eng. 2012 Aug;9(4):046001. doi: 10.1088/1741-2560/9/4/046001. Epub 2012 Jun 19.
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.
基于感觉运动节律的脑-机接口（BCI）的主体在实验过程中表现出性能的巨大变化。在这里，我们表明，源自额顶网络的高频 γ 振荡可以在逐次试验的基础上预测这种变化。我们将这一发现解释为注意力网络通过调制感觉运动节律对 BCI 性能的影响的经验支持。
