EEG-based subject- and session-independent drowsiness detection: an unsupervised approach

Authors:
Nikhil R. Pal;Chien-Yao Chuang;Li-Wei Ko;Chih-Feng Chao;Tzyy-Ping Jung;Sheng-Fu Liang;Chin-Teng Lin
Affiliations:
Dept of Comp Sci, Nat. Chiao-Tung Univ, Hsinchu, Taiwan and Brain Res. Center, Nat. Chiao-Tung Univ, Hsinchu, Taiwan and Comp. and Commun. Sci. Div., Elect. and Communi. Sciences Unit, Indian Stat ...;Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan;Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan;Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan;National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan and Institute for Neural Computation, University of California of San Die ...;Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, Taiwan;Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan and Brain Research Center, National Chiao-Tung University, Hsinchu, Taiwan
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

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Abstract

Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for driver's safety. Typically, physiological studies on real-time detection of drowsiness usually use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness implies that for many subjects, group statistics may not be useful to accurately predict changes in cognitive states. Researchers have attempted to build subject-dependent models based on his/her pilot data to account for individual variability. Such approaches cannot account for the cross-session variability in EEG dynamics, which may cause problems due to various reasons including electrode displacements, environmental noises, and skin-electrode impedance. Hence, we propose an unsupervised subject- and session-independent approach for detection departure from alertness in this study. Experimental results showed that the EEG power in the alpha-band (as well as in the theta-band) is highly correlated with changes in the subject's cognitive state with respect to drowsiness as reflected through his driving performance. This approach being an unsupervised and session-independent one could be used to develop a useful system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.