Citation

Li M, Fu JW, Lu BL. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008; 2008: 5000-5003.

Affiliation

Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240, China. mai_lm@sjtu.edu.cn

Copyright

(Copyright © 2008, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2008.4650337

PMID

19163840

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).

Language: en