Automatic discrimination of task difficulty predicted by frontal EEG activity during working memory tasks in young and elderly drivers.
Automatic discrimination of task difficulty predicted by frontal EEG activity during working memory tasks in young and elderly drivers.
复制标题
在年轻和老年驾驶员的工作记忆任务中，通过额叶脑电图活动自动区分任务难度。
DOI：
10.1142/s0219622022500201
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发表时间：
2022
影响因子：
4.9
通讯作者：
Koji Kashihara
中科院分区：
文献类型：
--
作者：
Koji Kashihara and Yoshitaka Matsuda;能登 香;Koji Kashihara
希望通过关注老年人的大脑特征来预防交通事故。驾驶过程中的注意力水平取决于信息处理资源的数量。本研究首先探讨了交通情景下的分级工作记忆任务中注意水平的变化对脑电波的影响。随着记忆负荷的增加，老年组的反应时比青年组延迟。在选择性任务中，困难任务主要激活老年人额中线区的诱导和功率。老年人的注意力水平可以保持，因为激活的慢脑电图反应，无论任务的性能，虽然增加的波可能反映困倦。由于基于驾驶员脑信号的辅助系统可以防止车祸，因此本研究还旨在评估从EEG信号中自动区分不同注意任务的分析方法。与最近邻和人工神经网络相比，支持向量机更准确地分类注意力水平（即，任务难度）在工作记忆任务中反映了诱导和波的变化。这一结果可以与脑机接口系统相关，以判断驾驶过程中的任务难度并提醒驾驶员注意危险。这项研究的实验任务是有限的，因为它们只涉及模拟，其中参与者识别引导板并删除不相关的信息。应该使用EEG数据来研究实时判断，以改进可以提醒驾驶员即将到来的危险的系统。
It is desirable to prevent traffic accidents by focusing on elderly people’s brain characteristics. The attention level during driving depends on the amount of information-processing resources. This study first aimed at investigating the effects of the change in attention levels on the electroencephalogram (EEG) waves during the graded working memory tasks for a traffic situation. With the increase in memory loads, reaction times were delayed in the elderly than the young group. The difficult tasks activated the inducedandpowers in the frontal midline area primarily in the elderly, during the selective task for a target. The elderly could retain the attention level because of the activated slow EEG responses, regardless of the task performance, although the increasedwave may reflect drowsiness. Because the assistance system based on drivers’ brain signals can prevent car accidents, this study also aimed at evaluating the analytical method to automatically discriminate the different attentional tasks from the EEG signals. Compared with-nearest neighbors and artificial neural networks, support vector machines more accurately classified attention levels (i.e., task difficulty) during working memory tasks reflecting a change in the inducedandwaves. This result can be related to a brain-computer interface system to judge the task difficulty during driving and alert a driver to danger. The experimental tasks for this study were limited because they involved simulations only in which participants recognized guided boards and removed irrelevant information. Real-time judgments should be investigated using EEG data to improve systems that can alert drivers to oncoming dangers.