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首页> 外文期刊>Frontiers in Human Neuroscience >Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles
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Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles

机译：在半自动车辆注意力分配模型中比较脑电图和低成本生理设备的相对强度

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As semiautonomous driving systems are becoming prevalent in late model vehicles, it is important to understand how such systems affect driver attention. This study investigated whether measures from low-cost devices monitoring peripheral physiological state were comparable to standard EEG in predicting lapses in attention to system failures. Twenty-five participants were equipped with a low-fidelity eye-tracker and heart rate monitor and with a high-fidelity NuAmps 32-channel quick-gel EEG system and asked to detect the presence of potential system failure while engaged in a fully autonomous lane changing driving task. To encourage participant attention to the road and to assess engagement in the lane changing task, participants were required to: (a) answer questions about that task; and (b) keep a running count of the type and number of billboards presented throughout the driving task. Linear mixed effects analyses were conducted to model the latency of responses reaction time (RT) to automation signals using the physiological metrics and time period. Alpha-band activity at the midline parietal region in conjunction with heart rate variability (HRV) was important in modeling RT over time. Results suggest that current low-fidelity technologies are not sensitive enough by themselves to reliably model RT to critical signals. However, that HRV interacted with EEG to significantly model RT points to the importance of further developing heart rate metrics for use in environments where it is not practical to use EEG.
机译：随着半自动驾驶系统在后期模型车辆中变得越来越普遍，重要的是要了解这种系统如何影响驾驶员的注意力。这项研究调查了在监测系统故障时，是否可以用低成本设备监测外围生理状态的措施与标准脑电图相媲美。 25名参与者配备了低保真眼动仪和心率监测器以及高保真NuAmps 32通道快速凝胶脑电图系统，并要求他们在完全自主的车道中进行检测以发现潜在的系统故障改变驾驶任务。为了鼓励参与者注意道路并评估其参与变道任务，参与者需要：（a）回答有关该任务的问题; （b）持续记录整个驾驶任务中展示的广告牌的类型和数量。进行了线性混合效应分析，以使用生理指标和时间段对自动化信号的反应反应时间（RT）的潜伏期进行建模。中线顶叶区域的α-带活动性与心率变异性（HRV）一起对随时间推移进行RT建模非常重要。结果表明，当前的低保真技术本身不够敏感，无法可靠地对关键信号进行RT建模。但是，HRV与EEG进行交互以显着地模拟RT的点表明了进一步开发心律指标以用于不适合使用EEG的环境的重要性。

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