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. 98~1. 95 Hz,3. 91~7. 81 Hz及7. 81~15. 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。
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 evaluate the operator＇s fatigue,anxiety and effort.
作者
王娆芬
顾幸生
陈敏
WANG Rao-fen1, GU Xing-sheng2 , CHEN Min1(1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; 2. School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237, China)
出处
《传感器与微系统》
CSCD
2018年第9期22-24,28,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61703269)
上海市自然科学基金资助项目(18ZR1416700)
关键词
眼电信号
小波变换
心理负荷
特征提取
相对能量
electrooculogram （EOG） signal
wavelet transform
mental workload
feature extraction
relative energy （RE）
