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

Chin Teng Lin, Nikhil R. Pal, Chien Yao Chuang, Li Wei Ko, Chih Feng Chao, Tzyy Ping Jung, Sheng Fu Liang

研究成果: Article同行評審

75 引文 斯高帕斯(Scopus)


Monitoring and prediction of changes in the human cognitive states, such as alertness and drowsiness, using physiological signals are very important for drivers 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 subjects 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.

期刊Eurasip Journal on Advances in Signal Processing
出版狀態Published - 2008

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 資訊系統
  • 硬體和架構
  • 電氣與電子工程


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