title: Estimating vigilance in driving simulation using probabilistic PCA.
abstract: In avoiding fatal consequences in accidents behind steering wheel caused by low level vigilance, EEG has shown bright prospects. In this paper, we propose a novel method for discriminating two different vigilance states of the subjects, namely wake state and sleep state, during driving a car in a simulation environment. After filtering the EEG data into a specific frequency band, we use probabilistic principle component analysis (PPCA) to reduce the data dimension. Then we model each vigilance state as a lower dimension Gaussian random variable by applying PPCA again. The feature related to class posterior probability is calculated for classification. The experimental results show satisfying time resolution (< or = 5s) and high accuracy (> or = 96%) across five subjects on both common frequency bands beta (19-26 Hz) and gamma (38-42 Hz), and a broad band (8-30 Hz).
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240, China. mai_lm@sjtu.edu.cn
authors.forename: Mu
authors.initials: M
authors.lastname: Li
authors.forename: Jia-Wei
authors.initials: JW
authors.lastname: Fu
authors.forename: Bao-Liang
authors.initials: BL
authors.lastname: Lu
mesh_terms: D001143:Arousal / Q000502:physiology*; D001288:Attention / Q000502:physiology*; D001334:Automobile Driving; D001921:Brain / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D025341:Principal Component Analysis; D013647:Task Performance and Analysis; D014851:Wakefulness / Q000502:physiology*