Dynamic clustering for vigilance analysis based on EEG.
Electroencephalogram (EEG) is the most commonly studied signal for vigilance estimation. Up to now, many researches mainly focus on using supervised learning methods for analyzing EEG data. However, it is hard to obtain enough labeled EEG data to cover the whole vigilance states, and sometimes the labeled EEG data may be not reliable in practice. In this paper, we propose a dynamic clustering method based on EEG to estimate vigilance states. This method uses temporal series information to supervise EEG data clustering. Experimental results show that our method can correctly discriminate between the wakefulness and the sleepiness for every 2 seconds through EEG, and can also distinguish two other middle states between wakefulness and sleepiness.
Shi
Li-Chen
LC
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China. lch-shi@sjtu.edu.cn
Lu
Bao-Liang
BL
D000328:Adult; D000465:Algorithms; D001143:Arousal / Q000502:physiology*; D016477:Artifacts; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014851:Wakefulness / Q000502:physiology*; D055815:Young Adult