Wavelet transform-based feature extraction of the operator’s electrooculogram signal under multi-tasks

WANG Rao-fen; GU Xing-sheng; CHEN Min

doi:10.13873/J.1000-9787(2018)09-0022-03

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Wavelet transform-based feature extraction of the operator’s electrooculogram signal under multi-tasks

更新时间：2025-09-11

- Wavelet transform-based feature extraction of the operator’s electrooculogram signal under multi-tasks
- Issue 9, Pages: 22-24+28(2018)
- 作者机构：
  
  1. 上海工程技术大学电子电气工程学院
  2. 华东理工大学信息科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2018)09-0022-03
  CLC： R318;TN911.7
- Published：2018
- 稿件说明：
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WANG Rao-fen, GU Xing-sheng, CHEN Min. Wavelet transform-based feature extraction of the operator’s electrooculogram signal under multi-tasks[J]. 2018, (9): 22-24+28.
DOI：

WANG Rao-fen, GU Xing-sheng, CHEN Min. Wavelet transform-based feature extraction of the operator’s electrooculogram signal under multi-tasks[J]. 2018, (9): 22-24+28. DOI： 10.13873/J.1000-9787(2018)09-0022-03.

摘要

为了从高精神负荷下的操作员眼电（EOG）信号中提取出能够反映疲劳焦虑和努力程度变化的显著性特征

通过实验

采集了6位被试人员在多级任务负荷下的EOG信号

采用小波变换方法对EOG信号进行了10层小波分解;选取能量较高的频段

根据小波系数计算了各频段的相对能量（RE）特征

分析了眼电特征与操作员的心理负荷之间的关系。结果表明:提取的EOG特征中

0. 98

. 95 Hz

3. 91

. 81 Hz及7. 81

5. 63 Hz频段的RE与操作员的心理负荷有显著的关系

提取的特征可用于操作员疲劳、焦虑和努力等的评估。

Abstract

In order to extract salient features from electrooculogram（ EOG） signals which can reflect the operator’s fatigue

anxiety and effort

EOG signals of 6 operators’ under multi-task conditions are collected. The wavelet transform method is adopted to decompose the EOG signals. Then the relative energy（ RE） of each sub-band is calculated. The correlations between EOG features and the operator ’s mental workload are analyzed. The experimental results show that in extracted features of EOG

in RE of frequency 0. 98

1. 95 Hz

3. 91

7. 81 Hz and 7. 81

15. 63 Hz are significantly relate to the operator’s mental workload. Candidate features are given to evalua

te the operator’s fatigue

anxiety and effort.

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