Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.
Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu 300, Taiwan.
Sensors (Basel). 2020 Jun 3;20(11):3169. doi: 10.3390/s20113169.
Substantial developments have been established in the past few years for enhancing the performance of brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user's mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user's visual area. BCI user's cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users' physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user's cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1-4 Hz), theta (4-7 Hz), and beta (13-30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1-4 Hz), alpha (8-12 Hz), and beta (13-30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user's cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.
在过去的几年中，基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）的性能得到了很大的提高。过去的 SSVEP-BCI 研究在许多不同的应用中使用了不同的目标频率和闪烁刺激。然而，当执行 SSVEP-BCI 任务时，识别用户的心理状态变化并不容易。我们所能观察到的是用户视觉区域中目标频率的 EEG 功率增加。BCI 用户的认知状态变化，特别是在精神集中状态或思维丧失状态，会影响 SSVEP 持续使用中的 BCI 性能。因此，如何通过探索 SSVEP 任务期间的神经活动变化来区分 BCI 用户的生理状态，是提高 BCI 性能的关键技术。在这项研究中，我们设计了一个新的 BCI 实验，将工作记忆任务与 SSVEP 任务的闪烁目标相结合，使用 12 Hz 或 30 Hz 频率。通过探索对应于工作记忆和 SSVEP 任务性能的 EEG 活动变化，我们可以识别用户的认知状态是在精神集中还是思维丧失状态。实验结果表明，在额叶，与思维丧失状态相比，精神集中状态下 delta（1-4 Hz）、theta（4-7 Hz）和 beta（13-30 Hz）EEG 活动增加更多。此外，与思维丧失状态相比，在枕叶，精神集中状态下 delta（1-4 Hz）、alpha（8-12 Hz）和 beta（13-30 Hz）频段的功率增加更多。此外，KNN 和贝叶斯网络分类器的平均分类性能在受试者中观察到为 77%至 80%。这些结果表明心理状态变化如何影响 BCI 用户的性能。在这项工作中，我们开发了一种新的方案来识别 BCI 任务期间用户的认知状态。这些发现可以作为未来 BCI 发展的新神经标记。
