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基于小波熵的视觉疲劳检测方法研究

发表时间:2013-12-15  浏览量:1303  下载量:531
全部作者: 王晶,韩丞丞,徐光华
作者单位: 西安交通大学机械工程学院
摘 要: 针对脑机接口(brain computer interface,BCI)实验中视觉疲劳难以检测的缺点,提出一种基于小波熵的定量疲劳检测算法。首先结合脑电信号(electroencephalogram,EEG)节律特性,利用小波包分解提取对疲劳状态变化敏感的α节律波,然后引进多尺度下的小波熵对α节律波进行分析,从而定量检测视觉疲劳。受试者实验后EEG的小波熵明显下降,实验分析结果表明:在一定的视觉疲劳状态下,α波的小波熵对疲劳状态的变化更加敏感,结果差异更加显著。此结果说明α波的小波熵可以反映出EEG的复杂度,对人体疲劳状态的变化敏感,计算简单,因此有望成为疲劳检测中的一种重要指标。
关 键 词: 基础医学其他学科;脑机接口;视觉疲劳;小波包分解;小波熵
Title: Research on visual fatigue detection based on wavelet entropy
Author: WANG Jing, HAN Chengcheng, XU Guanghua
Organization: School of Mechnical Engineering, Xi’an Jiaotong University
Abstract: It is rather difficult to detect visual fatigue in brain computer interface (BCI) experiments. In this paper, we proposed a quantitative fatigue detection algorithm based on wavelet entropy. Firstly, combining with the features of electroencephalogram (EEG) rhythm, wavelet package decomposition was used to extract α rhythm which was sensitive to fatigue. Then, wavelet entropy with multi-scale was employed to analyze α rhythm to quantitatively detect visual fatigue. The results showed that after BCI experiments, the wavelet entropy of subject’s EEG decreased dramatically. This indicated that wavelet entropy of α rhythm was more sensitive to human being’s fatigue and could reflect the complextiy of EEG. Furthermore, the computational load of wavelet entropy was fairly low and it was a promising indicator in fatigue detection.
Key words: other subjects of basic medicine; brain computer interface; visual fatigue; wavelet package decomposition; wavelet entropy
发表期数: 2013年12月第23期
引用格式: 王晶,韩丞丞,徐光华. 基于小波熵的视觉疲劳检测方法研究[J]. 中国科技论文在线精品论文,2013,6(23):2241-2245.
 
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