An EEG-based subject- and session-independent drowsiness detection

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data tb account for Individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subject's cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subject's changing level of alertness, indexed by subject's behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages3448-3454
Number of pages7
DOIs
StatePublished - 24 Nov 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: 1 Jun 20088 Jun 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period1/06/088/06/08

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    Lin, C-T., Pal, N. R., Chuang, C. Y., Jung, T. P., Ko, L-W., & Liang, S. F. (2008). An EEG-based subject- and session-independent drowsiness detection. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 3448-3454). [4634289] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4634289