Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety

Publication Type:
Conference Proceeding
Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 3233 - 3238
Issue Date:
Filename Description Size
asse.pdfPublished version593.34 kB
Adobe PDF
Full metadata record
© 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.
Please use this identifier to cite or link to this item: