Wirth Christopher, Toth Jake, Arvaneh Mahnaz
Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, United Kingdom.
Front Neurosci. 2020 Feb 11;14:66. doi: 10.3389/fnins.2020.00066. eCollection 2020.
Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.
研究已经证实，在导航任务中，大脑对观察正确和错误动作的反应是可以区分的。此外，这些分类可作为基于学习的脑机接口的反馈，使真实或虚拟机器人能够找到通往目标的近似最优路径。然而，在导航时，不仅要知道我们正朝着目标的正确方向移动，还要知道何时到达目标，这一点很重要。我们让参与者观察一个虚拟机器人执行一维导航任务。我们记录了脑电图（EEG），然后对两类正确动作的反应进行了神经生理学分析：一类是朝着目标靠近但未到达目标的动作，另一类是确实到达目标的动作。此外，我们在时域特征上使用逐步线性分类器，以便在单次试验的基础上区分这两类动作。还使用了第二个数据集来进一步测试这种单次试验分类。我们发现，在动作到达目标的情况下，P300的振幅明显更大。有趣的是，我们能够以66.5%和68.0%的平均总体准确率对观察这两类正确动作时诱发的脑电图信号进行相互分类，所有参与者的准确率均高于随机水平。作为概念验证，我们已经表明，使用单次试验脑电图对观察这些不同正确动作时的脑电图反应进行相互分类是可行的。这可以用作基于学习的脑机接口的一部分，并为更自主的脑机接口导航系统打开一扇新的大门。
