Ma Jia-Xin, Shi Li-Chen, Lu Bao-Liang
Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6591-4. doi: 10.1109/IEMBS.2010.5627122.
This study aims at using electrooculographic (EOG) features, mainly slow eye movements (SEM), to estimate the human vigilance changes during a monotonous task. In particular, SEMs are first automatically detected by a method based on discrete wavelet transform, then linear dynamic system is used to find the trajectory of vigilance changes according to the SEM proportion. The performance of this system is evaluated by the correlation coefficients between the final outputs and the local error rates of the subjects. The result suggests that SEMs perform better than rapid eye movements (REM) and blinks in estimating the vigilance. Using SEM alone, the correlation can achieve 0.75 for off-line, while combined with a feature from blinks it reaches 0.79.
本研究旨在利用眼电图（EOG）特征，主要是慢眼动（SEM），来估计在单调任务期间的人类警觉性变化。具体而言，首先通过基于离散小波变换的方法自动检测慢眼动，然后使用线性动态系统根据慢眼动比例来找到警觉性变化的轨迹。该系统的性能通过最终输出与受试者局部错误率之间的相关系数进行评估。结果表明，在估计警觉性方面，慢眼动比快速眼动（REM）和眨眼表现更好。仅使用慢眼动，离线时相关性可达0.75，而与眨眼特征相结合时则达到0.79。
