John Alka Rachel, Singh Avinash K, Do Tien-Thong Nguyen, Eidels Ami, Nalivaiko Eugene, Gavgani Alireza Mazloumi, Brown Scott, Bennett Murray, Lal Sara, Simpson Ann M, Gustin Sylvia M, Double Kay, Walker Frederick Rohan, Kleitman Sabina, Morley John, Lin Chin-Teng
IEEE Trans Neural Syst Rehabil Eng. 2022;30:770-781. doi: 10.1109/TNSRE.2022.3157446. Epub 2022 Mar 29.
Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.
现代工作环境与技术有着广泛的交互，任务的认知复杂度更高，这导致人类操作员的心理负荷增加。空中交通管制操作员经常在这样复杂的环境中工作，我们设计了跟踪和碰撞预测任务来模拟他们的基本任务。通过阐明这些任务中工作量变化的生理反应，来理清操作员所经历的工作量变化的影响。我们记录了24名参与者在执行具有三个难度级别的跟踪和碰撞预测任务时的脑电图（EEG）、眼动和心率变异性（HRV）数据。我们的研究结果表明，这两项任务中任务负荷的变化都能在EEG、眼动和HRV数据中得到灵敏反映。多元回归结果还表明，使用相应的EEG、眼动和HRV数据可以预测操作员在这两项任务中的表现。结果还表明，这些任务中的每一项任务期间的脑动力学都可以从相应的眼动、HRV和表现数据中估计出来。此外，跟踪和碰撞预测任务中工作量变化的神经测量指标明显不同，这表明神经测量指标可以为心理负荷的类型提供见解。这些发现适用于未来心理负荷自适应系统的设计，该系统在决定不仅“何时”而且“什么”进行自适应时整合神经测量指标。我们的研究为开发智能闭环心理负荷自适应系统的可行性提供了有力证据，该系统可确保复杂工作环境中的效率和安全。
