驾驶员认知分心的脑电图相关性分析与检测

项目名称： 驾驶员认知分心的脑电图相关性分析与检测

项目编号： No.31300852

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 医药、卫生

项目作者： 赵国朕

作者单位： 中国科学院心理研究所

项目金额： 21万元

中文摘要： 驾驶员在行车过程中由于思考问题等原因导致走神、注意力不集中的现象称之为认知分心。认知分心是一种典型的危险驾驶行为，对道路安全造成极大隐患。研究人员一直在寻求合理的认知分心检测方法，以便对处于认知分心状态的驾驶员进行提示和警告。现有的研究主要是基于眼动数据和驾驶参数的分心检测方法，无法直接地测量认知分心现象中的认知过程和心理状态的变化，且数据采集具有很大的局限性。本研究将采集驾驶员在虚拟驾驶任务中注意力集中和认知分心两种状态下的脑电信号，利用心理学知识分析多维脑电数据对分心现象的表征显著性；另一方面，本研究将通过机器学习的方法，以数据驱动的方式学习脑电数据中分心现象的特征模式，分析具有显著区分性的特征矢量，构造正常和认知分心状态的分类模型，最终设计实现一套准确、时实的驾驶员认知分心检测系统。

中文关键词： 驾驶员分心；认知分心；机器学习；驾驶安全；脑电

英文摘要： Driver cognitive distraction represents a shift of attention away from the primary driving task to some internal thinking unrelated to driving. It is a typical risky driving behavior and a great threaten to the road traffic safety. Researchers have been looking for reasonable methods to detect cognitive distraction and provide a driver a warning message when he or she is distracted. Existing studies of driver cognitive distraction mainly relied on eye movement and driving signals which are difficult to measure a driver's cognitive process and mental status directly or acquire data under certain conditions. This study will collect a driver's electroencephalography (EEG) data between intervals of attentive driving and cognitive distraction during simulated driving tasks and examine knowledge-based EEG indices of cognitive distraction. On the other hand, this work will apply machine learning, a data-driven technique to learn the EEG patterns of cognitive distraction, identify distinguishing features/vectors, and build a classification model between attentive driving and cognitive distraction. Eventually, this study will design an accurate real-time detection system of driver cognitive distraction.

英文关键词： driver distraction；cognitive distraction；machine learning；driving；EEG