Myrden Andrew, Chau Tom
IEEE Trans Neural Syst Rehabil Eng. 2017 Apr;25(4):345-356. doi: 10.1109/TNSRE.2016.2641956. Epub 2017 Jan 9.
Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks. Using a feature clustering algorithm to account for redundancy in EEG signal features, we classified self-reported changes in fatigue, frustration, and attention levels with 74.8 ± 9.1%, 71.6 ± 5.6%, and 84.8 ± 7.4% accuracy, respectively. Based on the most frequently-selected features across all participants, we note the importance of the frontal and central electrodes for fatigue detection, posterior alpha band and frontal beta band activity for frustration detection, and posterior alpha band activity for attention detection. Future work will focus on integrating these results with an active brain-computer interface.
传统的脑机接口性能往往会随时间推移而不稳定。最近有人提出，被动脑机接口可能为补充和稳定这些传统系统提供一种方法。在本研究中，我们探究了一种被动脑机接口的可行性，该接口利用脑电图在单次试验的基础上监测心理状态的变化。我们记录了15个位置的皮层活动，同时11名身体健全的成年人完成了一系列具有挑战性的心理任务。使用特征聚类算法来处理脑电图信号特征中的冗余信息，我们分别以74.8±9.1%、71.6±5.6%和84.8±7.4%的准确率对自我报告的疲劳、沮丧和注意力水平变化进行了分类。基于所有参与者最常选择的特征，我们注意到额叶和中央电极对疲劳检测的重要性、后α波段和额叶β波段活动对沮丧检测的重要性，以及后α波段活动对注意力检测的重要性。未来的工作将集中于将这些结果与主动脑机接口相结合。
