Fuzzy Neural Network based Real-Time Driver Fatigue Detection and Warning System using EEG Waves

Driving fatigue has been considered as a significant risk factor in transportation accidents, and the development of the human cognitive State based on Electro Encephalo Gram (EEG) has become a major focus in the field of driving safety for many drivers. In adaptive online prediction framework which is capable of capturing subject-specific predictive patterns autonomously by constructing a subject-specific pattern library, based on which a probabilistic prediction rule is established. A real-time wireless Electro Encephalo Gram (EEG) based Brain Computer Interface (BCI) system for drowsiness detection has been proposed to reduce the time delay. A new method for driver fatigue detection based on EEG waves using Recurrent Self-Evolving Fuzzy Neural Network (RSEFNN)is proposed. The model uses Mindwave to collect the attention waves of EEG from the left prefrontal lobe of the subject, and measures the data when the subject is in the State of concentration, relaxation, fatigue and sleep. The Mindwave device and Matlab is interfaced using the Think Gear Library module. The correlation coefficient of the driver attention is classified to detect driver fatigue. Thus the system using brain wave sensor warns the driver when he/she feels drowsy.