Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China.
J Neural Eng. 2023 Sep 6;20(5). doi: 10.1088/1741-2552/acf345.
. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions.To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days.. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation.Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.
. 警觉状态对于脑机接口 (BCI) 任务的有效表现至关重要，因此，研究 BCI 任务中的警觉水平是必不可少的。尽管如此，大多数研究都集中在驾驶任务中的警觉水平上，而不是 BCI 任务上，不同 BCI 任务中的警觉状态的脑电图 (EEG) 模式仍不清楚。本研究旨在确定不同 BCI 任务和会话中警觉估计的 EEG 模式和表现的相似性和差异性。. 为了实现这一目标，我们构建了一个基于稳态视觉诱发电位的 BCI 系统和一个基于快速序列视觉呈现的 BCI 系统，并招募了 18 名参与者在四天内进行四个 BCI 实验会话。. 我们的研究结果表明，高和低警觉水平的特定神经模式在各个会话中相对稳定。在所有频带和 delta 和 theta 频带中的不同 BCI 任务中，差分熵特征在不同的警觉水平之间有显著差异，theta 频带特征在警觉估计中起着关键作用。此外，前额叶、颞叶和枕叶区域与 BCI 任务中的警觉状态更为相关。我们的结果表明，跨会话的警觉估计比跨任务的估计更准确。. 本研究阐明了两种 BCI 任务中警觉状态的潜在机制，为 BCI 应用中的警觉估计进一步研究提供了基础。
