Monitoring Pilot's Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions
Monitoring Pilot's Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions
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DOI：
10.3390/s19061324
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发表时间：
2019-03-16
期刊：
影响因子：
3.9
通讯作者：
Lotte, Fabien
中科院分区：
文献类型：
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作者：
Dehais, Frederic;Dupres, Alban;Lotte, Fabien
最近的技术进步使得低成本和高度便携的大脑传感器的开发成为可能，例如预放大干电极，可以在实验室外测量认知活动。这项技术为在复杂的现实生活中（例如操作飞机时）监控“工作中的大脑”开辟了前景。然而，需要在实际操作条件下对这些传感器进行基准测试。因此，我们设计了一种场景，其中配备六干电极脑电图系统的 22 名飞行员必须执行一种低负载和一种高负载流量模式以及被动听觉怪异。在低负载条件下，参与者监视由飞行教练处理的飞行，而他们在高负载条件下驾驶飞机。在群体层面，统计分析显示，与高负载条件相比，低负载条件下听觉目标（Pz、P4 和 Oz 电极）的 P300 振幅更高，α 波段功率（Pz 电极）更高，Theta 波段功率（Oz 电极）更高。同时使用事件相关电位和事件相关频率特征的单次试验分类准确性没有超过区分两种负载条件的机会水平。然而，当仅考虑对连续信号计算的频率特征时，分类准确率平均达到 70% 左右。这项研究展示了干脑电图在高度生态和嘈杂的环境中监测认知的潜力，但也表明在将其用于日常飞行操作之前仍需要改进硬件。
Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the "brain at work" in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations.