n order to quickly and accurately identify the fatigue related phenomena such as the continuous attention level of the driver after long-time driving and his reaction time to emergencies, the present study establishes a recognition algorithm for recognition of driving- fatigue related problems based on EEG signal index combining the improved particle swarm algorithm with support vector machine, and sets up a method for classifying driving fatigue degree. According to the fatigue classification method, two hours are served as critical points to delineate mild fatigue and severe fatigue. The recognition rate of driver's severe fatigue by the algorithm is higher than that of driver's mild fatigue. The driver's fatigue perception (7.35) in the second phase is much greater than that of the first phase (2.11), and the average reaction time (640 ms) and speed deviation (3.8 km / h) of the second phase are also much greater than that of the first phase (510 ms) and (1.4 km / h), indicating that the driver experiences obvious fatigue after driving for two consecutive hours, and his ability to deal with emergencies and to control vehicle during severe fatigue decrease

EEG Signals, Driving Fatigue, Support Vector Machine, Continuous Attention Level, Reaction Time