Detecting driver sleepiness from EEG alpha wave during daytime driving

Drowsy driving is the main reason for sleep-related crashes. We have observed that an alpha wave attenuation-disappearance phenomenon and a typical alpha blocking phenomenon commonly exist in the eye closure events during daytime simulated driving experiments. These two alpha-related phenomena prove to respectively represent two different sleepiness levels: the sleep onset and the relaxed wakefulness. Therefore, we propose a novel algorithm for tracking the alpha wave change and detecting the two alpha-related phenomena in real-time for recognizing driver sleepiness. Our proposed algorithm adopts continuous wavelet transform for charactering the signal change and support vector machine for classification. The experimental results indicate that the algorithm is able to detect the start and end points of alpha waves during eye-closed period and distinguish the two types of end points of alpha waves in two alpha-related phenomena with high sensitivity and precision. The eye-closed period detected by our algorithm with alpha waves has a high overlapping rate with that marked by human experts. The main contributions of the proposed algorithm are twofold: to detect alpha waves during the eye-closed period in real-time and serve as an indicator for judging the current sleepiness level as the sleep onset or relaxed wakefulness at the end points of alpha waves.