Using EEG spectral components to assess algorithms for detecting fatigue

By B. T. Jap, Sara Lal, P. Fischer, E. Bekiaris. Expert systems with applications, 2009

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Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, background): “Performing a cognitively demanding task while driving influences both the driver's visual behavior and driving performance (as indicated by braking behavior).”
A driver fatigue recognition model based on information fusion and dynamic Bayesian network (2010, background): “[2] assessed the four electroencephalography (EEG) activities, (delta (d), theta (h), alpha (a) and beta (b)) during a monotonous driving session for 52 subjects (36 males and 16 females), and got the results for conditions stable delta and theta activities over time, a slight decrease of alpha acti”
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, methodology): “also noticed that conventional techniques like the fast Fourier transform (FFT) were mostly employed for detecting changes in the spectral components of the corresponding signals [17], [23].”