Faculty of EEMCS, University of Twente, NB Enschede, The Netherlands.
IEEE Trans Neural Syst Rehabil Eng. 2011 Dec;19(6):628-37. doi: 10.1109/TNSRE.2011.2166562. Epub 2011 Oct 6.
Brain-computer interfaces (BCIs) are known to suffer from spontaneous changes in the brain activity. If changes in the mental state of the user are reflected in the brain signals used for control, the behavior of a BCI is directly influenced by these states. We investigate the influence of a state of loss of control in a variant of Pacman on the performance of BCIs based on motor control. To study the effect a temporal loss of control has on the BCI performance, BCI classifiers were trained on electroencephalography (EEG) recorded during the normal control condition, and the classification performance on segments of EEG from the normal and loss of control condition was compared. Classifiers based on event-related desynchronization unexpectedly performed significantly better during the loss of control condition; for the event-related potential classifiers there was no significant difference in performance.
脑机接口（BCI）已知会受到大脑活动自发变化的影响。如果用户的精神状态变化反映在用于控制的脑信号中，那么 BCI 的行为将直接受到这些状态的影响。我们研究了在 Pacman 的变体中失去控制状态对基于运动控制的 BCI 性能的影响。为了研究暂时失去控制对 BCI 性能的影响，我们在正常控制条件下对脑电图（EEG）记录进行了 BCI 分类器训练，并比较了正常和失去控制条件下 EEG 片段的分类性能。基于事件相关去同步的分类器在失去控制条件下出人意料地表现得更好；对于事件相关电位分类器，性能没有显著差异。
