A noninvasive real-time driving fatigue detection technology based on left prefrontal Attention and Meditation EEG

Driving fatigue has been considered as a significant risk factor in transportation accidents, and the development of the human cognitive state based on electroencephalogram (EEG) has become a major focus in the field of driving safety. However, it faces portable and real-time problems on its practical application. This study uses MindWave to collect the Attention and Meditation EEG from the left prefrontal lobe of the subject, and uses the relation between Attention and Meditation EEG when the subject is in the state of concentration, relaxation, fatigue and sleep being measured first. As a result, a new method for driving fatigue detection based on the correlation coefficient between drivers Attention and Meditation EEG is proposed. Meanwhile, the k-Nearest Neighbors (k-NN) algorithm is introduced to classify the correlation coefficient between the drivers Attention and Meditation EEG, so as to detect driving fatigue and alert. Lastly, the 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 68.31% and 90.43% respectively.