An EEG-based forecasting system using a functional-link recurrent self-evolving fuzzy neural network was superior to state-of-the-art models for estimating driving fatigue.
An EEG-based forecasting system using FL-RSEFNN outperformed existing models in assessing mental fatigue during simulated driving.
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.
Liu et al. (Thu,) conducted a other in Mental fatigue during driving. FL-RSEFNN EEG-based forecasting system vs. State-of-the-art models was evaluated on Estimation of driving fatigue. An EEG-based forecasting system using a functional-link recurrent self-evolving fuzzy neural network was superior to state-of-the-art models for estimating driving fatigue.