A Safety Guard for Driving Fatigue Detection Based on Left Prefrontal EEG and Mobile Ubiquitous Computing

摘要

According to the portable and real-time problems on the driving fatigue prevention based on electroencephalogram (EEG), a headband integrated with Thinkgear EEG chip, tri-axial accelerometer, gyroscope and Bluetooth is developed to collect the subject's left prefrontal Attention, Meditation EEG and head movement data. The relation between Attention and Meditation EEG when the subject is in the state of concentration, relaxation, fatigue and sleep is analyzed firstly. As a result, a new method for driving fatigue detection based on the correlation coefficient between subject's Attention and Meditation EEG is proposed. Meanwhile, the slide windows and k-Nearest Neighbors (k-NN) algorithm are introduced to classify the correlation coefficient between the subject's Attention and Meditation EEG, so as to detect driving fatigue and alert. Lastly, a software running on an Android smart device is developed based on the above technologies, and the experiment proves that it has noninvasive and real-time advantages, while its sensitivity and specificity are 80.98% and 90.43% respectively.