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基于大脑源活动和ARMA模型的驾驶疲劳检测.

Fahimeh Nadalizadeh¹, Mehdi Rajabioun², Amirreza Feyzi³

¹Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Medical & biological engineering & computing

|December 20, 2023

概括

使用脑电图 (EEG) 信号进行准确的驾驶员疲劳检测对于道路安全至关重要. 这项研究引入了一种结合源本地化,ARMA建模和集体分类器的新方法,以改进疲劳识别.

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使用条款与条件

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
运输安全运输安全

背景情况:

驾驶员疲劳是道路交通事故的主要原因之一.
电脑电图 (EEG) 为客观检测疲劳提供了一个有前途的途径.

研究的目的:

开发和验证一种基于EEG的新方法,用于准确识别驾驶员疲劳.
通过减少与疲劳有关的事故来提高驾驶员的安全.

主要方法:

预处理EEG信号,包括过和文物排斥.
应用源定位和配合多变量自回归 (MVAR) 和自回归移动平均 (ARMA) 模型.
从模型参数,源关系和波形变换中提取特征.
采用特征选择方法 (RelifF,NCA) 和集体分类器 (KNN,SVM,NB) 与投票策略.

主要成果:

拟议的方法准确地识别和分类疲劳的司机.
与单个分类器相比,整体分类显著提高了疲劳检测性能.
在源活动和EEG信号之间使用ARMA建模的新方法被证明是有效的.

结论:

关键词:

驾驶疲劳导致的疲劳双卡尔曼波器可以使用.电脑电图 (电脑电图) 是一种脑电图.多变量自回归模型源代码本地化方法

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开发的基于EEG的方法为驾驶员疲劳检测提供了强大而准确的解决方案.
这种方法有可能在现实世界中用于提高道路安全.
进一步的研究可以探索先进的特征提取和分类技术.