Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (p-value = 0.05) than the subjects who do not have such connections.

在紧急情况下，尤其是在驾驶过程中，根据观察到的反应来描述神经活动图是一个具有挑战性的问题，这将有助于设计驾驶员辅助设备并更好地了解大脑。本研究旨在调查紧急制动过程中涉及哪些区域，测量连接的相互作用和强度，并通过我们从事件相关电位信号中提取的动态因果模型 (DCM) 参数描述这些大脑区域之间的耦合，然后估计这些区域基于具有视觉刺激的紧急制动数据。这些数据是从一项模拟器研究中重新分析的，该研究旨在为参与者在简单的驾驶任务中创造紧急情况。实验方案包括驾驶虚拟现实汽车，受试者在模拟器系统中暴露于紧急情况，同时记录脑电图、眼电图和肌电图信号。在这项研究中，使用多重稀疏先验方法从事件相关反应中识别蒙特利尔神经研究所坐标中活跃大脑区域的位置，其中传感器空间被分配到资源空间。源定位结果揭示了九个活跃区域。在对数据应用 DCM 后，获得了针对所有人的紧急制动期间的拟议模型。制动响应时间是根据诱导制动操作后第一个明显的（高于噪音水平）制动踏板偏转来定义的。结果显示，在视觉皮层、视觉处理、p值 = 0.05) 比没有这种联系的受试者。