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利用脑连接估计器提高基于 EEG 的驾驶员注意力分散分类。

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

机构信息

School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.

School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia.

出版信息

Sensors (Basel). 2022 Aug 19;22(16):6230. doi: 10.3390/s22166230.

DOI:10.3390/s22166230
PMID:36015991
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9414352/
Abstract

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

摘要

本文提出了一种基于脑连接估计器作为特征的 EEG（脑电图）驾驶员分神分类的新方法。10 名具有超过一年驾驶经验且平均年龄为 24.3 岁的健康志愿者参与了一个虚拟现实环境，该环境有两种状态，分别是简单的数学问题解决任务和车道保持任务，以模拟分神驾驶任务和非分神驾驶任务。对与额、中、顶、枕、左运动和右运动区域相关的六个选定组件的选定时段进行独立成分分析（ICA）。格兰杰-盖维克因果关系（GGC）、定向传递函数（DTF）、部分定向相干性（PDC）和广义部分定向相干性（GPDC）脑连接估计器用于计算连接矩阵。这些连接矩阵被用作特征，使用径向基函数（RBF）训练支持向量机（SVM），并对分心和非分心驾驶任务进行分类。GGC、DTF、PDC 和 GPDC 连接估计器的分类准确率分别为 82.27%、70.02%、86.19%和 80.95%。进一步分析了 PDC 连接估计器，以确定区分分心和非分心驾驶任务的最佳窗口。本研究表明，PDC 连接估计器可以产生更好的驾驶员分心分类准确性。

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