MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, PR China.
School of Psychology, University of Leeds, Leeds LS2 9JT, UK.
Accid Anal Prev. 2024 Nov;207:107769. doi: 10.1016/j.aap.2024.107769. Epub 2024 Sep 4.
Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems.
在驾驶过程中进行电话交谈或其他认知挑战性任务会对认知功能产生不利影响，并增加事故风险。现有的脑电图方法已经被证明可以区分负载和无负载，但不能区分不同水平的认知负载。此外，还没有研究过负载的脑电图测量是否可以用于预测关键事件中的安全结果。脑电图微状态分析将脑电图信号分类为一组简洁的原型功能状态，已在其他任务环境中得到很好的应用，但尚未应用于驾驶环境。在这里，通过驾驶模拟实验来解决这一差距。在追尾碰撞冲突之前和期间，测试了三种电话使用条件（不使用电话、免提和手持），以及两种任务难度水平（单个或两位数的加减）。分析了传统的脑电图频谱功率和脑电图微状态。结果表明，不同水平的认知负载对脑电图微状态的影响不同，而脑电图频谱功率不受影响。当驾驶员在驾驶时从事电话任务时，出现了一种独特的脑电图模式，表现为两个脑电图微状态同时增加和减少，这表明驾驶员更加关注听觉信息，可能会牺牲注意力重新定向的能力。这两个微状态的增加和减少遵循从基线到免提简单、免提复杂、手持简单、最后是手持复杂的单调序列，表现出对任务难度的敏感性。在主动车辆刹车前后都发现了这种模式。此外，在主动车辆刹车前的脑电图微状态提高了对安全结果的预测，即主动车辆刹车后最小车头时距，这清楚地表明这些微状态测量了大脑状态，表明驾驶受损。此外，脑电图微状态比任务难度更能预测安全结果，突出了任务效果的个体差异。这些发现增强了我们对分心驾驶中涉及的神经动力学的理解，并可用于评估车载系统引起的认知负载的方法。
