Open Access

EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach

  • Nikhil R. Pal1, 2, 3,
  • Chien-Yao Chuang1, 2,
  • Li-Wei Ko1, 2,
  • Chih-Feng Chao1, 2,
  • Tzyy-Ping Jung1, 2, 4,
  • Sheng-Fu Liang5 and
  • Chin-Teng Lin1, 2Email author
EURASIP Journal on Advances in Signal Processing20082008:519480

Received: 2 December 2007

Accepted: 22 July 2008

Published: 4 November 2008


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.

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Authors’ Affiliations

Department of Computer Science, National Chiao-Tung University
Brain Research Center, National Chiao-Tung University
Computer and Communication Sciences Division, Electronics and Communication Sciences Unit, Indian Statistical Institute
Institute for Neural Computation, University of California of San Diego
Department of Computer Science and Information Engineering, National Cheng-Kung University


© Nikhil R. Pal et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.