Using EEG spectral components to assess algorithms for detecting fatigue

Authors:
Budi Thomas Jap;Sara Lal;Peter Fischer;Evangelos Bekiaris
Affiliations:
Department of Medical and Molecular Biosciences, University of Technology, Sydney, Science Building 4, Level 6, Broadway, NSW 2007, Australia;Department of Medical and Molecular Biosciences, University of Technology, Sydney, Science Building 4, Level 6, Broadway, NSW 2007, Australia;Signal Network Technology Pty Ltd, Lane Cove, NSW 1595, Australia;Center for Research and Technology Hellas, Hellenic Institute of Transport 6th km, Charilaou-Thermi Road, 57001 Thermi, Greece
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Fatigue is a constant occupational hazard for drivers and it greatly reduces efficiency and performance when one persists in continuing the current activity. Studies have investigated various physiological associations with fatigue to try to identify fatigue indicators. The current study assessed the four electroencephalography (EEG) activities, delta (@d), theta (@q), alpha (@a) and beta (@b), during a monotonous driving session in 52 subjects (36 males and 16 females). Performance of four algorithms, which were: algorithm (i) (@q+@a)/@b, algorithm (ii) @a/@b, algorithm (iii) (@q+@a)/(@a+@b), and algorithm (iv) @q/@b, were also assessed as possible indicators for fatigue detection. Results showed stable delta and theta activities over time, a slight decrease of alpha activity, and a significant decrease of beta activity (p