Semi-supervised clustering for vigilance analysis based on EEG

Vigilance research is very useful and important to our daily lives. EEG has been proved very effective for measuring vigilance. Up to now, many researches mainly focus on using supervised learning methods to analyze the vigilance. However, the labelled information of vigilance is hard to get and sometimes not reliable. In this paper, we proposed a semi-supervised clustering method for vigilance analysis based on EEG. This method uses the insufficient labeled information to guide the vigilance related feature selection and uses prior knowledge of vigilance state transform to guide the clustering algorithm. The experiment results show that our method can almost correctly distinguish the awake state and the sleeping state by EEG, and can also represent the transform processes of reasonable middle states between the awake state and the sleeping state.