Abstract: Biomedical signals of human can reflect the body's task load, fatigue and other psychological information. Compared with other biomedical signals, electrooculogram (EOG) has higher amplitude, less interference, and is easy to detect. In this paper, the EOG signals of operator’s were analyzed. Wavelet transform was used to remove the high-frequency artifacts. Then fuzzy c-means was adopted to detect the eye blink peak points of EOG. After that, eye blink interval (EBI) of operator was calculated. Four EOG features (the average of EBI, variance of EBI, standard deviation of EBI and variation coefficient of EBI) were extracted. Finally, the relationship between EOG features and operator’s fatigue, effort, anxiety and task load were analyzed. The experimental results illustrate that EOG features had some relation to the operator’s fatigue, effort, anxiety and task load respectively.

Jiangwen Song, Raofen Wang, Guanghua Zhang, Chaoxing Xiong, Leyan Zhang and Cunbang Sun, “Electrooculogram Signals Analysis for Process Control Operator Based on Fuzzy c-Means” International Journal of Advanced Computer Science and Applications(IJACSA), 6(9), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060918

@article{Song2015,
title = {Electrooculogram Signals Analysis for Process Control Operator Based on Fuzzy c-Means},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2015.060918},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060918},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {9},
author = {Jiangwen Song and Raofen Wang and Guanghua Zhang and Chaoxing Xiong and Leyan Zhang and Cunbang Sun},
}