Vigilance Estimation Based on EEG Signals
In some tasks that require sustained attention, vigilance levels of the operator might become very important. EEG has been proved very effective for measuring vigilance. However, many difficulties exist in this field such as how to label the EEG data, how to remove the noise from the EEG data and so on. In this paper, we introduce a very useful signal transform method, Common Spatial Pattern, to process the EEG data. Also we use unsupervised learning methods for analyzing the EEG data under two extreme cases, sleeping and awake, and discard other middle vigilance states. The results of our experiments are quite promising and give a direction for the vigilance labelling and feature selection in the future work.
Hong Yu Li-Chen Shi Bao-Liang Lu
Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai, 200240, China
国际会议
北京
英文
1540-1546
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）