EEG potentials predict upcoming emergency brakings during simulated driving
EEG potentials predict upcoming emergency brakings during simulated driving
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DOI：
10.1088/1741-2560/8/5/056001
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发表时间：
2011-10-01
影响因子：
4
通讯作者：
Blankertz, Benjamin
中科院分区：
文献类型：
--
作者：
Haufe, Stefan;Treder, Matthias S.;Blankertz, Benjamin
紧急制动辅助有可能防止大量的汽车碰撞。最先进的系统分两个阶段运作。一旦外部传感器显示可能发生碰撞，就会采取基本的安全措施。如果检测到制动踏板的进一步活动，系统将自动执行紧急制动。在这里，我们提出的驾驶模拟器的研究结果表明，驾驶员的意图进行紧急制动可以检测基于肌肉激活和大脑活动之前的行为反应。使用脑电图（EEG）和肌电图（EMG）获得了相同的预测准确性水平，前者比后者工作得更快，后者比踏板动力学工作得更快。使用EEG和EMG的模拟辅助系统被发现比仅依赖于踏板响应的系统早130 ms检测紧急制动。在100 km h（-1）的行驶速度下，这相当于将制动距离减少3.66 m。这一结果激发了神经人体工程学的方法来驾驶辅助。我们的脑电图分析产生了一个特征性的事件相关的潜在签名，包括组件相关的感官注册的一个关键的交通情况，心理评估的感官知觉和运动准备。虽然所有这些组件应该经常发生在正常驾驶过程中，我们推测，这是他们的特点时空叠加在紧急制动的情况下，导致相当大的预测性能，我们观察到。
Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h(-1) driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.