The Center of Collaboration and Innovation, Jiangxi University of Technology, Nanchang 330098, Jiangxi, China.
College of Economics and Management, Shenyang Aerospace University, Shenyang 110136, Liaoning, China.
Comput Intell Neurosci. 2022 Aug 23;2022:9775784. doi: 10.1155/2022/9775784. eCollection 2022.
The objective of this article is to solve the current social phenomenon of a large number of fatigue driving, so that social safety becomes more stable in the future, and the detection and application of driving fatigue are more meaningful. This article aims to study the application of graph neural network (GNN) in driving fatigue detection (this article is abbreviated as DFD) based on EEG signals. This article uses a pattern classification method based on a multilayer perceptual overlimit learning machine to find the hidden information of the signal through an unsupervised learning self-encoding structure, which achieves the optimization purpose and has a better classification effect than traditional classifiers. An improved soft threshold (the soft threshold can be used to solve the optimization problem, and the optimization problem solved is similar to the base pursuit noise reduction problem, but it is not the same, and it should be noted that the soft threshold cannot solve the base pursuit noise reduction problem) denoising algorithm is selected, and the collected EEG (a technique for capturing brain activity using electrophysiological markers is the electroencephalogram). The sum of the postsynaptic potentials produced simultaneously by a large number of neurons occurs when the brain is active. It records the process of brain activity in the cerebral cortex or scalp surface) signals are preprocessed, so that the feature extraction efficiency of extracting EEG signals is improved. The final experimental data show that the traditional support vector machine, SVM algorithm, and the KNN convolutional neural (the K-nearest neighbor method, often known as KNN, was first put forth by Cover and Hart in 1968. It is one of the most straightforward machine learning algorithms and a theoretically sound approach) algorithms has a recognition rate of 79% and 81% for fatigue. The improved algorithm in this article has an average recognition rate of 87.5% for driver fatigue, which is greatly improved.
本文旨在解决当前社会大量疲劳驾驶的现象，使未来社会安全更加稳定，对驾驶疲劳的检测和应用更有意义。本文旨在研究基于脑电信号的图神经网络（GNN）在驾驶疲劳检测（简称 DFD）中的应用。本文采用基于多层感知机的超限学习机的模式分类方法，通过无监督学习自编码结构寻找信号的隐含信息，达到优化目的，分类效果优于传统分类器。选择了改进的软阈值（软阈值可以用来解决优化问题，所解决的优化问题类似于基追踪降噪问题，但并不相同，需要注意的是，软阈值不能解决基追踪降噪问题）降噪算法，对采集到的脑电（一种利用电生理标记物捕捉大脑活动的技术是脑电图）信号进行预处理，提高提取脑电信号的特征提取效率。最终的实验数据表明，传统的支持向量机、SVM 算法和 KNN 卷积神经网络（K 近邻法，简称 KNN，由 Cover 和 Hart 于 1968 年首次提出。它是最简单的机器学习算法之一，也是一种理论上合理的方法）对疲劳的识别率分别为 79%和 81%。本文改进的算法对驾驶员疲劳的平均识别率为 87.5%，有了很大的提高。
