Wirth Christopher, Toth Jake, Arvaneh Mahnaz
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3050-3053. doi: 10.1109/EMBC44109.2020.9176230.
Studies have shown the possibility of using brain signals that are automatically generated while observing a navigation task as feedback for semi-autonomous control of a robot. This allows the robot to learn quasi-optimal routes to intended targets. We have combined the subclassification of two different types of navigational errors, with the subclassification of two different types of correct navigational actions, to create a 4-way classification strategy, providing detailed information about the type of action the robot performed. We used a 2-stage stepwise linear discriminant analysis approach, and tested this using brain signals from 8 and 14 participants observing two robot navigation tasks. Classification results were significantly above the chance level, with mean overall accuracy of 44.3% and 36.0% for the two datasets. As a proof of concept, we have shown that it is possible to perform fine-grained, 4-way classification of robot navigational actions, based on the electroencephalogram responses of participants who only had to observe the task. This study provides the next step towards comprehensive implicit brain-machine communication, and towards an efficient semi-autonomous brain-computer interface.
研究表明，在观察导航任务时自动产生的大脑信号有可能作为机器人半自主控制的反馈。这使得机器人能够学习到通向预期目标的近似最优路线。我们将两种不同类型导航错误的子分类与两种不同类型正确导航动作的子分类相结合，创建了一种四路分类策略，提供了有关机器人执行动作类型的详细信息。我们采用了两阶段逐步线性判别分析方法，并使用来自8名和14名参与者观察两个机器人导航任务时的大脑信号进行了测试。分类结果显著高于随机水平，两个数据集的平均总体准确率分别为44.3%和36.0%。作为概念验证，我们已经表明，基于仅需观察任务的参与者的脑电图反应，对机器人导航动作进行细粒度的四路分类是可行的。这项研究朝着全面的隐式脑机通信以及高效的半自主脑机接口迈出了下一步。
