Department of Psychology and Ergonomics, Berlin Institute of Technology, Berlin, Germany.
Hum Factors. 2011 Apr;53(2):168-79. doi: 10.1177/0018720811400601.
This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver's mental workload index.
The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring.
An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions.
The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power.
These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent.
One potential future application of this study is to establish a general driver workload estimator that uses EEG signals.
本研究旨在探讨基于脑电图（EEG）的方法来推导驾驶员心理工作量指数的可行性。
生理信号为实验室和真实环境中的人体功能状态评估以及在驾驶员和车辆之间建立新的通信通道提供了敏感信息，该通道允许监测驾驶员的工作量。
进行了一项结合变道任务和 n-back 任务的实验。任务负荷水平在两个维度上进行了操纵，驾驶任务负荷和工作记忆负荷，每个维度包含三个任务负荷条件。
在工作记忆负荷维度上，额叶θ活动显示出显著增加，但在驾驶任务负荷维度上没有发现差异。然而，当两个维度的任务负荷增加时，顶叶α活动显著降低。还发现了与任务相关的差异。驾驶任务负荷对α功率的变化贡献更多，而工作记忆负荷对θ功率的变化贡献更多。此外，这两个任务负荷维度对θ和α功率都产生了显著的交互作用。
这些结果表明，即使不同 EEG 参数的敏感性往往取决于任务，EEG 技术也可以为驾驶员工作量检测提供敏感信息。
本研究的一个潜在应用是建立使用 EEG 信号的通用驾驶员工作量估计器。
