Ladouce S, Torre Tresols J J, Goff K Le, Dehais F
Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Human Factors and Neuroergonomics, Institut Superieur de l'Aeronautique et de l'Espace, Toulouse, France.
J Neural Eng. 2025 May 20;22(3). doi: 10.1088/1741-2552/add771.
Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies.Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (= 16) during two sessions of a 45 min sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent.Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience.This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving.
在许多关键操作中,长时间保持警觉至关重要,但由于所需的认知资源,这仍然具有挑战性。疲劳和其他因素会导致警觉性波动,使注意力焦点从与任务相关的信息上转移。这种注意力的失误在长时间任务中很常见,会导致绩效下降和错过关键信息,可能产生严重后果。识别预测注意力不集中的生理标志物是制定预防策略的关键。先前的研究已确定,脑电图(EEG)对周期性视觉刺激的反应,即稳态视觉诱发电位(SSVEP),是注意力的敏感标志物。在本研究中,我们评估了一种基于SSVEP的微创方法,用于在45分钟持续视觉注意力任务(麦夸里时钟任务)的两个阶段中跟踪16名健康参与者的警觉性。一个14赫兹的频率标记闪烁要么叠加在任务上,要么不存在。结果显示,在注意力失误之前,SSVEP反应较低,而其他脑电图频谱标志物,如额叶θ波和顶叶α波活动,不能可靠地区分检测到的和错过的注意力探测。重要的是,闪烁不会影响任务绩效或参与者体验。这种非侵入性的频率标记方法提供了一种连续的警觉性测量方法,能有效检测长时间任务中的注意力失误。它有望集成到被动脑机接口中,为空中交通管制或驾驶等高风险环境中的实时警觉性监测提供一种实用解决方案。