EEG-based Cognitive State Monitoring and Predition by Using the Self-Constructing Neural Fuzzy System

Abstract

Driver's cognitive state monitoring has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. In our past studies, we found that the EEG power spectrum changes were highly correlated with the driver's driving behavior performance. In this study, we attempt to construct an EEG-based self-constructing neural fuzzy system to monitor and predict the driver's cognitive state. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. Our experimental results including correlation and prediction show that the performances of our proposed system are significantly higher than using the traditional neural networks. Besides, the proposed EEG-based self-constructing neural fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.